Automatic flight management in an online marketplace

ABSTRACT

A database search system includes a database of search terms, each search term associated with a bid amount payable by an advertiser of a plurality of advertisers and a search engine responsive to search queries from searchers for searching the database. A flight management agent is responsive to advertiser-specified parameters for adjusting bid amounts of search listings to manage expenditures over a time interval.

RELATED APPLICATIONS

This application is a continuation in part of application Ser. No. 09/918,241 entitled SYSTEM AND METHOD FOR INFLUENCING A POSITION ON A SEARCH RESULT LIST GENERATED BY A COMPUTER NETWORK SEARCH ENGINE, filed on Jul. 30, 2001 in the names Davis, et al., which application is commonly assigned with the present application and incorporated herein in its entirety by this reference and which is a continuation of application Ser. No. 09/322,677, filed May 28, 1999, in the names of Darren J. Davis, et al., now U.S. Pat. No. 6,269,361.

REFERENCE TO COMPUTER PROGRAM LISTINGS SUBMITTED ON COMPACT DISK

A compact disc appendix is included containing computer program code listings pursuant to 37 C.F.R. 1.52(e) and is hereby incorporated by reference in its entirety. The total number of compact discs is 1 including 31,822 files and 156,346,522 bytes. The files included on the compact disc are listed in a file entitled “dir_s” on the compact disc. Because of the large number of files contained on the compact disc, the required listing of file names, dates of creation and sizes in bytes is included in the file dir_s on the compact disk and incorporated by reference herein.

BACKGROUND

The method described in the U.S. Pat. No. 6,269,361 can be burdensome to manage for an advertiser. In particular, advertisers want to maintain favorable positions in the search results (so as to obtain a high volume of qualified traffic) at a favorable price. The system described in U.S. Pat. No. 6,269,361 provides no ready means to do that. Advertisers can resort to frequent inspection of their ranking on search terms that are important to them, for example by performing a search on www.overture.com. When they observe a change as a consequence of competing advertisers' bidding activities, they can log in to the pay for placement website and change their bids manually in response. In the case where they have been outbid for a position they want to retain, they can increase their bid to retake the position if the required cost per click (“CPC”), which is equal to the amount of their bid, is one they are willing to pay. In the case where the bid of the listing ranked below theirs has decreased, some advertisers may wish to lower their bid to reduce the amount they pay while still maintaining their position in the results set.

There are many other tasks that a large advertiser, or an advertising agency acting on behalf of a large advertiser, must perform. Large advertisers usually manage advertising “flights.” A flight is typically concerned with a business activity promoting a particular set of products over some specific time interval. A flight typically has a budget, flight duration, and business objective, e.g., signing up 10,000 new credit card customers.

Creating a flight typically involves choosing a media outlet (TV, radio, print, online, etc.), and signing a contract that specifies the campaign (e.g., number of TV spots, their length, time of airing, and the markets in which they will be broadcast), customer exposure (e.g., number of viewers reached), and the cost. For online cost per impression (“CPM”) markets, this typically involves identifying the web pages on which the banner ads will run, their frequency, the expected number of impressions over the flight, and the CPM cost. Once the contract terms have been settled on, there is not much work that is required by an advertiser to manage a flight, other than getting periodic reports on customer exposure.

It requires considerably more effort for an advertiser to manage flight in a CPC marketplace. The input parameter to flight management in a CPC marketplace are: 1) the flight duration (start and end dates and times), 2) the flight budget, 3) the set of listings to be used in the CPC marketplace (each listing has a term, title, description, and URL), and 4) the maximum average CPC for the listings (any higher CPC will result in a loss to the advertiser).

There are other inputs that depend on the CPC marketplace: 1) the bids for all ranks for the terms of the listings (e.g., it takes $4.43 to be at rank 3 for the term “web hosting”), and 2) the number of clicks at every rank of every term (e.g., the term “travel” at rank 2 has 1800 clicks/day).

The goals of flight management in a CPC marketplace are to: 1) spend the budget evenly over the flight, 2) spend the budget exactly by the end of the flight (or as close to it as possible), 3) ensure that the average bid for the selected listings is less than the maximum average CPC, 4) ensure that the advertiser profit is maximized. This means that for the fixed flight budget the advertiser receives the maximum possible clicks. The advertiser will receive the maximum number of clicks if the average CPC for all the listings is the lowest. Since the flight budget is fixed, having the maximum possible clicks is equivalent to maximizing the advertiser ROI.

Managing a flight in a CPC marketplace requires: 1) selecting the listings to bid on 2) selecting the bid amount for each selected listing (which determines the rank of every listing at the current time), and 3) periodically re-evaluating the bids to make sure the flight is still on track. Spending can go over or under the projections in the middle of a flight because of the dynamics of the CPC marketplace.

A CPC marketplace requires considerably more effort from an advertiser to manage a flight compared to other alternatives (TV, radio, print, other online, etc.) This is because: 1) the advertiser must generate a large number of listings, 2) the advertiser must determine the maximum CPC to remain profitable, 3) the advertiser must determine how to maximize profit by selecting the optimal bids for every listing, and 4) the advertiser must continually monitor and adjust the bids throughout the flight—all of which requires considerable effort. In some cases this can take multiple employees working full-time. The next few paragraphs briefly describe these difficulties.

An advertiser must generate a large set of listings (this can be in the hundreds). For each listing the advertiser must select a term, title, description, and URL. For example, a listing may have the term “LCD Projector”, the title “Your Super Source for LCD Projectors”, the description “Infocus Proxima Sanyo Toshiba, Panasonic Hitachi. Incredible deals on the best in computer projection with the best support in the business”, and a URL pointing to the advertiser's web site.

This can be time consuming, as each listing typically requires a manual review process by the CPC marketplace operator to ensure that the title and description accurately reflect the destination URL and that the title and description are relevant to the term of the listing.

The advertiser must determine the maximum amount to bid on the listings, in order to ensure that there will not be a loss. The amount paid to transfer a searcher to an advertiser's web site must be less than the expected profit/click. This requires computing the conversion rate for all possible actions-computing the probability that a searcher will perform each of the possible actions once he is transferred to the advertiser's web site. The possible actions can include registering, buying an item, applying for a loan, etc. The conversion rates for the various actions are combined with the average profit/action to compute the expected profit/click.

For example, if 1% of the searchers transferred to an advertiser's web site buy a digital camera, which results in an average profit to the advertiser of $100, then the maximum CPC for the listing is $1.00 (anything higher will result in a loss).

In addition, an advertiser must set the CPC for every listing in order to maximize profit. A higher CPC for a listing results in a better rank, which generally leads to more clicks, but the average cost/click goes up (due to the higher CPC). The total profit is the product of 1) the number of clicks and 2) the average profit/click minus the average cost/click.

A higher CPC may or may not result in higher profit. In general, the total profit can go up or down with changes in rank. The following example shows the non-monotonic behavior of the total profit with rank for a single listing. The advertiser has an average profit/click of $4.90. Bidding to be at rank 6 results in the maximum profit to the advertiser, when budget for the listings is greater than or equal to $46. On the other hand, if budget limit is $20, then the optimal profit is at rank 8, since that rank has the highest profit while still coming under the budget.

Rank # Clicks CPC Profit/Click Total Profit Traffic Cost 1 18 $4.90 $4.90 $0.00 $88.20 2 17 $4.80 $4.90 $1.70 $81.60 3 16 $4.79 $4.90 $1.76 $76.64 4 15 $4.78 $4.90 $1.80 $71.70 5 12 $4.70 $4.90 $2.40 $56.40 6 10 $4.60 $4.90 $3.00 $46.00 7 5 $4.40 $4.90 $2.50 $22.00 8 4 $4.30 $4.90 $2.40 $17.20 9 3 $4.20 $4.90 $2.10 $12.60

Figuring out the optimal bid for a listing is a trial and error process, if the marketplace operator does not publicize the average number of clicks as a function of rank for every term. In this example, the advertiser may start at rank 3, then try rank 4, which results in an increase in total profit, continue to examine rank 5, etc. Another problem is that finding a local maximum may miss the rank with optimal profit—rank 6 may have higher profit than its neighbors, but it is possible that rank 10 is better than rank 6.

To complicate matters, the conversion rate for different listings may not be the same. For example, a listing with the term “auto” may have 1% of the searchers buying information about the dealer costs for a car, while for a listing with the term “car” this may be 1.5%. This difference may be a function of the term of the listing, or its title, description, or the web page pointed to by the URL. Also, the conversion rates for a single listing may depend on the rank at which the listing is displayed. For example, it could be that when the listing is displayed at rank one 2% of the searchers buy the product, while if the listing is at rank five only 1% of the searchers buy the product.

The advertiser must manage the listings on a regular basis. This is to ensure that the budget is spent evenly or unevenly as desired, and that the entire budget is spent by the end of the flight. Spending the entire budget before the end of the flight can result in misallocation of resources—too many customers followed by none—and not spending the entire budget can result in missed revenue opportunities.

An advertiser must make an initial estimate of the optimal bids for all listings, and then continually monitor these to ensure that the flight is on track. There are many reasons why the bids may need to be modified over time. The initial estimates of the number of clicks at different ranks for every listing may be incorrect, the initial estimates of the conversion rates may be incorrect or the conversion rates may change over time. For example the listings may have relevance to a particular date (e.g., Father's Day), which has just passed.

In addition, due to the dynamics of the marketplace, the number of clicks for every rank of every term can change. For example, the listing with the term “auto” may have of bid of $1.43 and be at rank 3. Later another advertiser enters the market with a bid of $1.44 to displace the listing at rank 3 to rank 4. At rank 4 the “auto” listing will have fewer clicks-the cost/click for the term “auto” has just gone up in the marketplace. Other changes may be caused by advertisers leaving the marketplace, existing advertisers increasing/decreasing their bids, and the marketplace operator increasing/decreasing the number of searchers performing search (e.g., by adding or dropping affiliates). The dynamics of the CPC marketplace may result in these changes at any time.

There are other marketplace conditions that advertisers must keep track of in order to maximize profit. These include checking if the bid of a listing is too high for its current rank. For example, an advertiser A₁ may set the CPC of a listing to $0.50 for the listing to be at rank 2—advertiser A₂ is at rank 3 with a CPC of $0.49. A few hours later, A₂ changes the CPC of his listing to $0.45, while still remaining at rank 3. Advertiser A₁ can now reduce the CPC of his listing from $0.50 to $0.46, while still maintaining the listing at rank 2.

The previous examples illustrate the various actions that advertisers must perform manually to manage their flights. Some advertisers do these tasks several times a day. Some advertisers have a plurality of employees dedicated to the management of their participation in a pay for placement marketplace, monitoring the positions of their listings and adjusting their bids, managing their budget, etc.

The manual process of polling the status of listings, checking the competitors in the marketplace, and checking the account status is time consuming and wasteful. Therefore, a need exists for a method and apparatus for advertisers to manage their advertising flights more effectively.

There is a need to provide an automated flight management system for advertisers. Such a system would take as input the parameters of the advertising flight: the budget, duration, terms, maximum average CPC, conversion rates, and average profit/action for each term. The system would automatically set the CPC for every listing in order to maximize the advertiser's profit, and spend the budget over the flight as specified by the advertiser. The system would periodically recompute the CPC of listings, taking into account the historic performance of the flight to better optimize profit. The system would periodically generate reports on the performance of the automated flight management system and communicate these to the advertiser. The system should give advertisers the ability to customize the automated flight plan generated by the system.

If these inefficiencies are not addressed by a marketplace operator, then an economic incentive remains for advertisers to produce automated services of their own to interact with the account management systems of the marketplace operator to obtain the economic advantage available relative to the limited automated services provided by the marketplace operator. As a further consequence, such a situation provides economic incentive for third parties to produce automated services for advertisers, for a fee, or a cut of the alleged savings produced. This is already happening.

BRIEF SUMMARY

By way of introduction only, the present embodiments may be referred to collectively as Automatic Flight Management. Automatic Flight Management is an improvement on existing pay for placement marketplace systems. In the basic marketplace system, an advertiser logs on to the advertiser interface and manages his advertising campaign by examining the marketplace information and the information related to his listings. For example, an advertiser can identify a set of terms, their description, and the CPC for each term, which is the amount that the advertiser will pay if a searcher clicks on the listing. An advertiser can also check the number of clicks at different ranks for a search term, examine the other competitive listings for a term, etc. Subsequently, when a search term matches a search query received from a searcher, the advertiser may give economic value to the marketplace operator. To manage an advertising flight, an advertiser also has to ensure that the budget is being spent according to plan throughout the flight.

The embodiments described herein use the concept of a bid, which corresponds to the economic value that an advertiser will give when network locations associated with the advertiser are referred to a searcher in response to a query from the searcher. The economic value may be a money amount charged or chargeable to the advertiser, either directly or indirectly. The economic value may be an amount debited from an account of the advertiser. The amount may be a money amount or another value, such as credit points. The economic value may be given by the advertiser to the operator of a database search system or to a third party.

The economic value is given when a searcher is referred to one or more network locations, such as advertiser web sites, are referred to a searcher. The referral may be made by presenting the network locations on a screen used for data entry and receipt by the searcher, alone or with other search results. This is referred to as an impression. Alternatively, and in an embodiment generally described herein, the referral may occur when the searcher clicks on or clicks through to access the network locations of the advertiser, as will be described in greater detail below. Or the referral may be by some other action taken by the searcher after accessing the network locations of the advertiser.

The embodiments herein automate many of the steps performed by an advertiser in managing an advertising flight. Currently an advertiser must periodically examine the state of his listings, the state of the marketplace, the past expenditures and conversion rates of searchers clicking through to an advertiser's web site, and whether the business objectives of the flight are being met. This manual examination of the marketplace and adjustment of the bids of the listings is time consuming and error prone, as an advertiser may have to guess at the number of clicks that a listing will receive at different ranks, if this information is not provided by the marketplace operator.

The disclosed embodiments of Automatic Flight Management enable an advertiser to specify the parameters of the advertising flight. The system provides an automated agent that acts on behalf of the advertiser, periodically checking the conditions in the marketplace and making adjustments to the bids of the listings to ensure that the objectives of the flight are met and that the advertiser profit is maximized.

The agent is a software process or application operating in conjunction with data maintained by the marketplace system. If all is well—the flight expenditures are on schedule and the current bids for the listings maximize advertiser profit—then the agent takes no further action. Otherwise, the agent adjusts the bids of one or more listings to get the flight expenditures in line with the budget and to maximize advertiser profit, and sends a message to alert the advertiser of the changes. Messages can be sent whenever adjustments are made, or they can be aggregated and sent periodically, at the control of the advertiser.

Inputs, Outputs, and Goals

An advertiser describes the parameters of the advertising flight by specifying:

-   -   1. {T₁, T₂, . . . , T_(k)}: the listings and their terms     -   2. B: the budget for the flight     -   3. I: the flight interval (starting and ending dates and times)     -   4. one or more of the following:         -   a. C: the maximum average CPC over all the terms         -   b. R: the conversion rate, which is the fraction of the             searchers that-perform an action at the advertiser's web             site, and         -   c. P: the average profit/action. The expected profit/click             is P×R

It is assumed that the budget B is to be spent evenly over the flight. If this is not the case, the advertiser can specify a collection of separate flights, where each flight has an even spend rate. For example, suppose that the flight budget is $100,000, where $20,000 is to be spent in month one, $30,000 is to be spent in month two, and $50,000 is to be spent in month three. The uneven flight can be broken into three separate flights with even spend rates: Flight one for the first month with a budget of $20,000, Flight two for the second month with a budget of $30,000, and Flight three for the third month with a budget of $50,000.

The marketplace operator has access to the current state of the marketplace, which can be used to optimize the flight. This includes:

-   -   1. b_(i,j): the bid for term T_(i) at rank j     -   2. c_(i,j): the number of clicks for term T_(i) at rank j

The Automatic Flight Management algorithm is run periodically to optimize the flight on an ongoing basis. At each step, the agent acting on behalf of the advertiser selects the optimal bid for every term in {T₁, T₂, . . . , T_(k)}. The bid selected for term T_(i) is b_(i), which is expected to result in c_(i) clicks from searchers over the flight interval.

For every term T_(i) in {T₁, T₂, . . . , T_(k)}, given its bid b_(i) and its estimated number of clicks c_(i) it is possible to determine:

-   -   1. the estimated total number of clicks from searchers over the         flight interval c_(t)=c₁+c₂+ . . . +c_(k).     -   2. the estimated total expense of the flight to the advertiser         e_(t)=b₁c₁+b₂c₂+ . . . +b_(k)c_(k), which must be less than the         budget B.     -   3. the estimated average bid over the flight         $b_{a} = {\frac{e_{t}}{c_{t}} = {\frac{{b_{1}c_{1}} + {b_{2}c_{2}} + {\cdots\quad b_{k}c_{k}}}{c_{1} + c_{2} + {\cdots\quad c_{k}}} \approx \frac{B}{c_{t}}}}$     -   4. the estimated number of actions taken by searchers at the         advertiser's web site A_(t)=c_(t)×R=(c₁+c₂+ . . . +c_(k))×R     -   5. the total advertiser profit, which is the sum of the profit         for every term. The profit for term T_(i) is the product of 1)         the number of clicks and 2) the profit/click minus the         cost/click, which is c_(i)(P×R−b_(i)). Therefore, the total         profit is         $T = {\sum\limits_{i = 1}^{k}{c_{i}{\left( {{P \times R} - b_{i}} \right).}}}$         This is approximately equal to c_(t)×P×R−B, if the total cost         over the flight $\sum\limits_{i = 1}^{k}{c_{i} \times b_{i}}$         is approximately equal to the flight budget B.

The total advertiser profit for the flight, T≈c_(t)×P×R−B, can be optimized by maximizing the total number of clicks c_(t). This is because the average profit/action P and the conversion rate R are constants, and the goal is to spend close to the constant flight budget B. The total number of clicks is approximately equal to the budget divided by the average bid, that is $c_{t} \approx {\frac{B}{b_{a}}.}$ Therefore, we can maximize the total number of clicks c_(t) by minimizing the average bid b_(a).

The goals of Automatic Flight Management are to:

-   -   1. spend the budget B evenly over the flight. This is         accomplished by setting the bids for the terms {T₁, T₂, . . . ,         T_(k)} so that the total cost is approximately equal to the         budget, that is b₁c₁+b₂c₂+ . . . +b_(k)c_(k)≈B.     -   2. optimize the advertiser's profit. This is accomplished by         maximizing the total number of clicks c_(t) while spending the         budget B. This is equivalent to minimizing the average bid b_(a)         while spending the budget B.     -   3. If the advertiser specifies a maximum average CPC C, then         ensure that         $b_{a} = {\frac{e_{t}}{c_{t}} = {\frac{{b_{1}c_{1}} + {b_{2}c_{2}} + {\cdots\quad b_{k}c_{k}}}{c_{1} + c_{2} + {\cdots\quad c_{k}}} \leq {C.}}}$     -   4. If the advertiser specifies a conversion rate R and the         average profit/action P, then use this information to select the         bids for the terms so that the total profit         $T = {\sum\limits_{i = 1}^{k}{c_{i}\left( {{P \times R} - b_{i}} \right)}}$         is maximized. This is different from condition 2, since it may         be possible to get a higher total profit without spending the         entire budget B.         Multiple Actions by a Searcher

It is possible that a searcher can perform one or more actions with economic value to the advertiser at his web site. For example, an advertiser can buy a TV, buy a TV and an extended service contract, or register to receive future promotional information. In general there will be a number of independent actions <α₁, α₂, . . . , α_(n)>, where the profit for the actions is <p₁, p₂, . . . p_(n)>, and the conversion rate for the actions is <r₁, r₂, . . . r_(n)>. That is, a searcher clicking through to an advertiser's web site has the probability r₁ of performing action α₁, and the probability r₂ of performing action α₂, etc. In this case, the average profit/click is ${P \times R} = {\sum\limits_{i = 1}^{n}{p_{i} \times {r_{i}.}}}$ Different Conversion Rates for Different Terms and Ranks

It is also possible that different terms have different conversion rates. So instead of having a single conversion rate R, the advertiser specifies a conversion rate R(T_(i)) for every term T_(i), in {T₁, T₂, . . . , T_(k)}. Let R_(max) be the highest conversion rate for the term T_(j).

We can standardize all terms to have the same conversion rate R_(max) by:

-   -   1. scaling the number of clicks of every rank of every term         T_(i) in {T₁, T₂, . . . , T_(k)} by the factor         $\frac{R\left( T_{i} \right)}{R_{\max}},$         and     -   2. scaling the bid of every rank of every term T_(i) in {T₁, T₂,         . . . , T_(k)} by the factor         $\frac{R_{\max}}{R\left( T_{i} \right)}$

Scaling the number of clicks normalizes the conversion rate of every term, and the inverse scaling of the bids ensures that the cost of a rank remains unchanged.

It is also possible that every rank of every term has a different conversion rate. So instead of having a single conversion rate R, the advertiser specifies a conversion rate R(T_(i),j) for every term T_(i) in {T₁, T₂, . . . , T_(k)} at every rank j. Let R_(max) be the highest conversion rate for the term T_(j) at rank 1. Similar to the previous case, we can standardize all terms to have the same conversion rate R_(max) at every rank by:

-   -   1. scaling the number of clicks of every term T_(i) in {T₁, T₂,         . . . , T_(k)} at every rank j by the factor         $\frac{R\left( {T_{i},j} \right)}{R_{\max}},$         and     -   2. scaling the bid of every term T_(i), in {T₁, T₂, . . . ,         T_(k)} at every rank j by the factor         $\frac{R_{\max}}{R\left( {T_{i},j} \right)}$         Optimal Flight Management

In the first embodiment of Automatic Flight Management, the system takes as input from an advertiser: 1) a set of listings for the flight with the terms {T₁, T₂, . . . , T_(k)}, 2) the budget B, 3) the maximum CPC C, and 4) the conversion rate R and average profit/action P.

This embodiment is optimal, since it examines all possible combination of bids for the terms {T₁, T₂, . . . , T_(k)}, and selects the combination that results in the highest profit to the advertiser, without going over the budget or going over the maximum average CPC.

For any term T_(i) in {T₁, T₂, . . . , T_(k)}, the advertiser can be at rank 1 by bidding $0.01 over the existing bid of the advertiser at rank 1. In this example we have assumed that the marketplace operator has selected the minimum bid increment of $0.01, but this could be arbitrary. Suppose that the current listing at rank 2 has a bid of $0.87. The advertiser can be at rank 2 with a bid of $0.88—unless there is an existing listing with a bid of $0.88, in which case the advertiser cannot be at rank 2! This process is repeated by examining the existing listing at rank 3, 4, . . . , etc., until all ranks have been examined. This process is repeated for every listing in {T₁, T₂, . . . , T_(k)}.

As we consider all combination of bids for the terms in {T₁, T₂, . . . , T_(k)}, suppose that in the current combination each term T_(i) has bid b_(i) and expected clicks is c_(i). The total clicks for the current combination is c_(t)=c₁+c₂+ . . . +C_(k), the total expense for the current combination is e_(t)=b₁×c₁+b₂×c₂+ . . . +b_(k)×c_(k), and the average bid for the current combination is $b_{a} = {\frac{e_{t}}{c_{t}} = \frac{{b_{1} \times c_{1}} + {b_{2} \times c_{2}} + {\cdots\quad b_{k} \times c_{k}}}{c_{1} + c_{2} + {\cdots\quad c_{k}}}}$

The current combination is discarded if:

-   -   1. the total cost is greater than the budget (e_(t)>B), or     -   2. the average CPC is greater than the maximum (b_(a)>C)

Otherwise, the system checks to see if the current combination is the best so far. If the current combination is the best so far, then the new best combination is set to the current combination.

If the advertiser only specifies a maximum average CPC C, then the current combination is the best if the total clicks c_(t) is greater than the total clicks of the previous best combination.

If the advertiser specifies the conversion rate R and the average profit/action P, then the current combination is the best if the current total profit, c_(t)(P×R−b_(a)), is greater than total profit of the previous best combination.

After computing the total advertiser profit for bidding at all possible ranks for the terms {T₁, T₂, . . . , T_(k)}, the system returns the optimal combination that results in the highest advertiser profit.

The following example illustrates the operation of Optimal Flight Management. Consider the case where there are two terms {T₁, T₂}, where each term has the following rank/clicks/bid combinations available for a new advertiser (we have simplified the example by having both terms be identical):

Rank Clicks Bid 1 1782 $0.72 2 1434 $0.71 3 973 $0.70 4 641 $0.68 5 457 $0.65 6 527 $0.64 7 466 $0.62 8 458 $0.53 9 389 $0.51 10 436 $0.44

Optimal flight management considers 100 combinations, where each combination picks one of the ten ranks/bids for each term. The optimal combinations for different budgets is shown below:

Terms Clicks Bid Cost [T1@1, T2@1] 3564 $0.7200000 $2,566.08 [T1@2, T2@1] 3216 $0.7155410 $2,301.18 [T1@2, T2@2] 2868 $0.7100000 $2,036.28 [T1@4, T2@1] 2423 $0.7094181 $1,718.92 [T1@3, T2@2] 2407 $0.7059576 $1,699.24 [T1@6, T2@1] 2309 $0.7017410 $1,620.32 [T1@7, T2@1] 2248 $0.6992705 $1,571.96 [T1@8, T2@1] 2240 $0.6811518 $1,525.78 [T1@10, T2@1] 2218 $0.6649594 $1,474.88 [T1@10, T2@2] 1870 $0.6470481 $1,209.98 [T1@8, T2@3] 1431 $0.6455905 $923.84 [T1@10, T2@3] 1409 $0.6195458 $872.94 [T1@8, T2@4] 1099 $0.6174886 $678.62 [T1@10, T2@4] 1077 $0.5828412 $627.72 [T1@10, T2@6] 963 $0.5494496 $529.12 [T1@8, T2@8] 916 $0.5300000 $485.48 [T1@10, T2@8] 894 $0.4861074 $434.58

For example, with a budget B of $1,250, the optimal flight that maximizes the advertiser's profit involves being at rank 10 for T₁ (with a bid of $0.44) and being at rank 2 for T₂ (with a bid of $0.71). The total cost for this flight is approximately $1,209.98, and the total number of expected clicks is 1,870. From this it follows that the average bid is 1,209.98/1870=0.6470481.

Unfortunately, this algorithm has exponential cost. Suppose that the k terms in {T₁, T₂, . . . , T_(k)} each have approximately r ranks. The algorithm must consider all combinations of ranks to bid at. There are r+1 possibilities for each term (to not bid on this term, or to bid at rank 1, or to bid at rank 2, . . . , or to bid at rank r). Since the choice for each term is independent, there are (r+1)^(k) possibilities to considering. Consequently, this algorithm is impractical for large problems.

Periodic Re-execution

At the start of the flight, the system computes the ideal combination of bids for the terms in {T₁, T₂, . . . , T_(k)}, as described above. Because of the dynamics of the marketplace, the bids and number of clicks for the various ranks can change during a flight.

The system periodically re-computes the optimal bids to ensure that the advertiser's flight remains on track to spend the budget evenly, and to ensure that the advertiser's profit continues to be maximized.

The marketplace operator, or the advertiser, can select the best times to re-plan the flight for an advertiser. This could be every hour, every day, or it could be when an event occurs that could change the bids or number of clicks (e.g., the marketplace operator increases the number of searchers significantly by adding a new high-traffic affiliate).

Before the algorithm is re-run the information about the flight is updated to reflect the remaining flight:

-   -   1. the budget B is replaced by the remaining budget     -   2. the flight duration I is replaced by the remaining flight         duration     -   3. the conversion rate of every term {T₁, T₂, . . . , T_(k)} at         every rank is replaced by the actual conversion rates observed         by the advertiser from the start of the flight—if sufficient         data has been accumulated to be statistically relevant.     -   4. the number of clicks/time for every term {T₁, T₂, . . . ,         T_(k)} at every rank is replaced by the actual number of         clicks/time from the start of the flight—if sufficient data has         been accumulated to be statistically relevant.     -   5. the bid for every term at every rank is updated with latest         information from the marketplace. For example, if the top bid         for the term “car” is $1.34, then it is possible to be at rank 1         for “car” with a bid of $1.35.

It is possible that at the start of the flight the budget is $30,000 for 30 days, and after one day the budget is $28,500 for 29 days. In this case the initial cost estimates were too low (underestimating the number of clicks, or some competing advertisers have dropped out, thus increasing rank). The algorithm is re-run with the latest information to make the best decisions going forward.

Price Protection

Price Protection is a variation of the first embodiment, where the system sets a price-protected bid for every selected rank of every term in {T₁, T₂, . . . , T_(k)}, instead of setting a fixed CPC bid. This is an improvement over the first variation, since it provides another opportunity to optimize the advertiser's profit in response to changing in bids in the marketplace in between the periodic re-execution of the flight management.

Suppose that the optimal combination of bids for the terms {T₁, T₂, . . . , T_(k)} from the most recent execution of Automatic Flight Management is {b₁, b₂, . . . , b_(k)}. Instead of setting a fixed bid b_(i) for the term T_(i), the system sets a Price Protected (PP) bid.

A PP bid indicates that the advertiser is willing to pay up to the maximum b_(i) for the term T_(i) and wishes the system to place him at the best rank possible with this bid, but to reduce the bid if it is possible to do so while maintaining its position at the best possible rank. It may be possible to reduce the CPC, for example, if the advertiser one rank below reduces his bid or drops out. PP can adjust the CPCs of advertisers on an ongoing basis, e.g., every time any bid changes.

Having the system continually adjust the bids to be at the best rank at the lowest possible cost to the advertiser ensures that the advertiser's profit is maximized on an ongoing basis. The system can adjust the bids immediately upon changes in other bids in the marketplace, without requiring the computationally more expensive task of re-executing automatic flight management.

Guided Flight Management

Guided Flight Management is a computationally efficient variation of Optimal Flight Management, where the system uses heuristics to get close to an optimal flight. While Guided Flight Management may be optimal for some cases, this cannot be guaranteed in general.

For most problems of any reasonable size, the cost of optimal flight management is intractable. Consequently, Guided Flight Management is an improvement that generates close to optimal results at a manageable cost.

As described earlier, generating the highest number of clicks for the budget maximizes the total advertiser profit. This is equivalent to having the lowest average bid for all the terms being bid on, while spending the total flight budget.

The heuristic used by Guided Flight Management is to explore combinations that lead to the least increase in the average bid. While Optimal Flight Management examines bidding at all possible combination of ranks for the terms in {T₁, T₂, . . . , T_(k)}, Guided Flight Management only explores a small subset of these.

The preferred embodiment for Guided Flight Management uses a hill-climbing algorithm to increase efficiency. There are many variations of such “greedy” algorithms that would be obvious to one ordinarily skilled in the art.

The system starts by considering the combination of ranks for the terms in {T₁, T₂, . . . , T_(k)} that results from making the lowest possible bids. The marketplace operator can choose the minimum bid, e.g., it can be $0.05 for every term. The “current combination” and “best combination” are assigned this initial combination.

The system next goes through a number of iterations, and at each iteration it explores all single-step extensions to the “current combination”. Each single step extension improves the rank of one of the terms in the “current combination” to the next-better rank available by increasing its bid by the minimum to get to this next better rank. For example, if in the “current combination” term T₁ has a bid of $0.05 and is at rank 40, and there is another advertiser with a bid of $0.07 who is at rank 39, then the next better rank possible for T₁ is rank 39, with a minimum bid of $0.08. So one of the extensions considered is improving the rank of T₁ from 40 to 39 by increasing the bid from $0.05 to $0.08. All single step extensions for the other terms in {T₂, T₃, . . . , T_(k)} are explored similarly.

The extension that results in the lowest average bid is the new value of the “current combination.”

When a new value of “current combination” is found, the system checks if it should update its “best combination” to be the “current combination”. In addition, the system checks if it should terminate and return the “best combination” or if it should continue the iteration process to find the next “current combination.”

If the advertiser only specifies a maximum average CPC C, then the “current combination” is the best if the total clicks, c_(t) is greater than the total clicks of the previous “best combination.”

If the advertiser specifies the conversion rate R and the average profit/action P, then the current combination is the best if the current total profit, c_(t)(P×R−b_(a)), is greater than total profit of the current “best combination.”

If the “current combination” is better than the previous “best combination”, the algorithm replaces the “best combination” with the “current combination.”

The algorithm terminates and returns the current “best combination”, if any of the following is true:

-   -   1. the total cost is greater than the budget (e_(t)>B ),     -   2. the average bid is greater than the maximum (b_(a)>C), or     -   3. there are no more one-step extensions to the “current         combination.” This is true if every term in {T₁, T₂, . . . ,         T_(k)} is bid to rank 1.

Otherwise, the algorithm proceeds with the next iteration to find the next value for the “current combination.”

The following example illustrates the operation of Guided Flight Management. The same data is used for the terms {T₁, T₂}, as presented earlier for Optimal Flight Management. The answers returned by Guided Flight Management for different budgets is given below:

Terms Clicks Bid Cost [T1@1, T2@1] 3564 $0.7200000 $2,566.08 [T2@2, T1@1] 3216 $0.7155410 $2,301.18 [T1@1, T2@3] 2755 $0.7129365 $1,964.14 [T2@4, T1@1] 2423 $0.7094181 $1,718.92 [T2@6, T1@1] 2309 $0.7017410 $1,620.32 [T1@1, T2@7] 2248 $0.6992705 $1,571.96 [T2@8, T1@1] 2240 $0.6811518 $1,525.78 [T1@2, T2@8] 1892 $0.6664270 $1,260.88 [T2@8, T1@3] 1431 $0.6455905 $923.84 [T1@4, T2@8] 1099 $0.6174886 $678.62 [T1@6, T2@8] 985 $0.5888528 $580.02 [T2@8, T1@7] 924 $0.5753896 $531.66 [T1@8, T2@8] 916 $0.5300000 $485.48 [T1@8, T2@10] 894 $0.4861074 $434.58 [T2@10, T1@10] 872 $0.4400000 $383.68

For example, with a budget B of $2,100, Guided Flight Management finds the solution: being at rank 1 for T₁ (with a bid of $0.72) and being at rank 3 for T₂ (with a bid of $0.70). The total expected cost for this flight is $1,964.14, and the total expected clicks is 2,755. From this it follows that the average bid is 1,964.14/2755=0.7129365.

For this same example, Optimal flight management will find the solution: being at rank 2 for T₁ and T₂ (with a bid of $0.71 for each). The expected total cost is $2,036.28, and the expected total clicks is 2,868. The average bid is $0.71. Note that Optimal Flight Management has a lower average bid ($0.71 vs. $0.7129365), which results in more clicks (2,868 vs. 2,755), though with a slightly higher expected cost ($2,036.28 vs. $1,964.14).

Guided Exploration is a hill-climbing algorithm—starting at the combination with the lowest bid, and then moving one step from the current combination to the neighboring combination that has the best value.

Hill-climbing exploration is much more efficient than checking every combination, as it has polynomial complexity. Suppose that the k terms in {T₁, T₂, . . . , T_(k)} each have approximately r ranks. At each iteration the algorithm will examine k single-step extensions to the current combination (and pick the one having the lowest average bid as the new “current combination”), and it will perform k×r iterations (going from the worst rank for all terms in {T₁, T₂, . . . , T_(k)} to the best). This results in a total time cost of k²×r.

Guided Exploration may not be optimal for some inputs, since it does not consider bidding at all combination of ranks for the terms {T₁, T₂, . . . , T_(k)}. Therefore, it cannot guarantee to find the combination of bids that maximizes the advertiser's profit for the flight. However, the heuristic to search for the combinations with the lowest bid results in solutions that may be optimal and that are often very close to optimal.

Optimizations

In another embodiment of the invention, there are a number of optimizations that are applied to Guided Flight Management. The optimized embodiments have the advantage of reduced computation time and space.

The optimizations ignore considering some of the ranks of the input terms {T₁, T₂, . . . , T_(k)} that are unlikely to be a part of the final solution, given the flight budget B and other constraints from the advertiser. This has the advantage of significantly reducing the computation time and computation space. These optimizations can be applied to both Optimal Flight Management and Guided Flight Management.

The first optimization ignores ranks of the terms {T₁, T₂, . . . , T_(k)} with fewer clicks than some threshold. This is to ignore ranks that cannot contribute significantly to sending searchers to an advertiser's web site.

The second optimization only considers a band of possible ranks for each term {T₁, T₂, . . . , T_(k)}. For example, the advertiser may specify that no ranks worse than rank 3 should be considered for any term, or specify that for term T₁ only ranks 3 through 10 should be considered, while for all other terms only ranks 2 through 5 should be considered. These constraints enable an advertiser to better accomplish his business goals, e.g., projecting an image of higher quality and trust by being at the premium ranks, or being at better ranks than its competitors.

The third optimization is a variation where the system automatically decides the band of possible ranks to consider for each term {T₁, T₂, . . . , T_(k)}. As automatic flight management is run periodically throughout a flight, the results of the previous run are used to select the appropriate band of ranks to consider for each term {T₁, T₂, . . . , T_(k)}.

The previous run of flight management picks a specific bid/rank for every term {T₁, T₂, . . . , T_(k)}, which is expected to maximize the advertiser's profit. The next run of automatic flight management only considers a window of ranks around the selected rank of the previous run. This window can be selected by the marketplace operator, e.g., selecting a window of 5 ranks to either side of the optimal rank from the previous run of automatic flight management. The larger the window the better the results, but the greater the computation time and cost for the marketplace operator.

For every term T_(i) in {T₁, T₂, . . . , T_(k)} the optimization ignores all ranks r_(i,j) of T_(i) when:

-   -   1. rank r_(i,j) is more than some constant α ranks away from the         rank of T_(i) with the previous optimal bid b_(i,o), or     -   2. the bid b_(i,j) of rank r_(i,j) is not within a factor β of         the previous optimal bid b_(i,o), that is, delete rank r_(i,j)         if the following is not true: (1−β)*b_(i,o) ≦b         _(i,j)≦(1+β)*b_(i,o). Here 0≦β≦1.

The first time automatic flight management is run, there are no previous optimal bids b_(i,o) for every term T_(i) in {T₁, T₂, . . . , T_(k)}. For the very first run, we can estimate the previous optimal bids b_(i,o) to be equal to the average optimal bid b_(o) for all terms {T₁, T₂, . . . , T_(k)}. The average optimal bid is computed such that if all the terms {T₁, T₂, . . . , T_(k)} are assigned this bid, then this will result in spending close to the budget B. The first time automatic flight management is run, ranks “far” from this initial estimate are not considered.

The following example illustrates the process of automatically pruning ranks. There are three terms {hotel, travel,vaction}. Suppose that the budget B is $525. Then the resulting average optimal bid b_(O) is $0.45, and by bidding on all terms at this level we approximately spend the entire budget.

Hotel Travel Vacation Rank Cost Clicks Bid Rank Cost Clicks Bid Rank Cost Clicks Bid 1 $979.12 1176 $0.83 1 $1,677.91 2326 $0.72 1 $393.58 353 $1.11 2 $990.22 1199 $0.83 2 $1,264.76 1799 $0.70 2 $191.34 179 $1.07 3 $399.90 500 $0.80 3 $502.11 1115 $0.45 3 $58.60 77 $0.76 4 $300.80 381 $0.79 4 $237.18 518 $0.46 4 $66.99 88 $0.76 5 $110.71 153 $0.72 5 $233.55 543 $0.43 5 $97.80 151 $0.65 6 $74.45 106 $0.70 6 $163.56 394 $0.42 6 $70.68 127 $0.56 7 $73.73 113 $0.65 7 $31.92 82 $0.39 7 $33.89 66 $0.51 8 $200.19 308 $0.65 8 $40.13 111 $0.36 8 $19.17 41 $0.47 9 $67.49 104 $0.65 9 $65.97 187 $0.35 9 $30.84 73 $0.42 10 $19.83 31 $0.64 10 $65.07 187 $0.35 10 $20.78 51 $0.41 11 $32.59 51 $0.64 11 $47.59 138 $0.34 11 $25.60 64 $0.40 12 $16.38 26 $0.63 12 $32.68 97 $0.34 12 $16.47 42 $0.39 13 $73.03 117 $0.62 13 $27.53 84 $0.33 13 $24.00 63 $0.38 14 $125.99 204 $0.62 14 $20.72 65 $0.32 14 $13.15 35 $0.38 15 $6.80 13 $0.52 15 $31.26 100 $0.31 15 $9.57 27 $0.35 16 $3.16 8 $0.40 16 $3.62 12 $0.30 16 $2.45 7 $0.35 17 $4.68 13 $0.36 17 $4.20 14 $0.30 17 $1.38 4 $0.35 18 $2.64 8 $0.33 18 $5.35 18 $0.30 18 $3.66 11 $0.33 19 $5.25 16 $0.33 19 $6.12 21 $0.29 19 $1.31 4 $0.33 20 $0.96 3 $0.32 20 $7.52 26 $0.29 20 $2.24 7 $0.32 21 $— 0 $— 21 $4.83 17 $0.28 21 $1.91 6 $0.32

Rank 16 of Hotel, rank 3 of Travel, and rank 8 of vacation have bids closest to $0.45. The boldfaced ranks are the ones remaining after the pruning process, when α=5 (all ranks more than 5 ranks away from the yellow rank are deleted) and β=1 (all ranks with bids greater than $0.90 are deleted). Other ranks are pruned for different values of α and β.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram illustrating the relationship between a large network and one embodiment of the system and method for generating a pay-for-placement search result of the present invention;

FIG. 2 is a chart of menus, display screens, and input screens used in one embodiment of the present invention;

FIG. 3 is a flow chart illustrating the advertiser user login process performed in one embodiment of the present invention;

FIG. 4 is a flow chart illustrating the administrative user login process performed in one embodiment of the present invention;

FIG. 5 is a diagram of data for an account record for use with one embodiment of the present invention;

FIG. 6 is a flow chart illustrating a method of adding money to an account record used in one embodiment of the present invention;

FIG. 7 illustrates an example of a search result list generated by one embodiment of the present invention;

FIG. 8 is a flow chart illustrating a change bids process used in one embodiment of the present invention;

FIG. 9 illustrates an example of a screen display used in the change bids process of FIG. 8; and

FIGS. 10-32 are flow diagrams illustrating operation of a system in accordance with the present embodiments.

DETAILED DESCRIPTION OF THE PRESENTLY PREFERRED EMBODIMENTS

The database search system includes in this embodiment a database of search listings. Each search listing is associated with a respective advertiser and each search listing includes a search term and a variable cost per click (CPC) or a variable display rank. The database search system in this embodiment further includes a search engine configured to identify search listings matching a search query received from a searcher. The matching search listings are preferably ordered in a search result list according to the display rank and the bid amount of the matching search listings. An agent is responsive to a condition definition from an advertiser to provide condition update information to the advertiser. The condition definition specifies a condition to be monitored. The condition update information, if present, specifies the circumstances under which the condition will be updated.

Another embodiment is implemented as a method for operating a database search system. In this embodiment, the method includes storing a plurality of search listings in a database. Each search listing is associated with an advertiser who gives economic value when a search listing is referred to a searcher. The method further includes determining a display position for associated search listings. In one example, the associated search listings are associated by common data, such as a search term or proximity to a search term. The display position may be determined in any appropriate way, from ways, which are completely deterministic to ways, which are completely random. The position determining way may be based on advertiser input or some other information. In one embodiment, each search listing is assigned a cost per click (CPC) and the display position is determined based on CPC, with the highest CPC listing for a search term being listed highest when that search term or a variant thereof is received. The method further includes receiving from an advertiser an indication of search listings for which the advertiser desires automatic flight management. The indication and the status reports to an advertiser may be sent according to any suitable communication method any available, convenient communication channel.

The procedures illustrated in FIGS. 10-24 may be performed in software or hardware or any combination of these. In one embodiment, the procedures are initiated as software procedures running on the processing system 34 of the account management server 22 (FIG. 1). In other embodiments, the procedures may run on a separate machine with network access to the search listings database. The procedures together form an Automatic Flight Management function.

The procedures illustrated in FIGS. 10-24 implement an automatic flight management system in a computer database system. The method includes acts such as receiving a automatic flight management instructions from an owner associated with some search listings stored in the computer database system, monitoring the performance of the flight and making any adjustments necessary to optimize the owner/advertiser's total profit, and sending a notification to the owner periodically and upon any changes regarding the status of the flight.

In one embodiment, the computer database system is a pay for placement search system as described herein and includes a database of search listings and a search engine. The search listings are each associated with an advertiser or owner of the search listing. The search listings each include data such as a search term, a bid amount or maximum cost per clickthrough specified by the advertiser, a cost per clickthrough (CPC) and a rank or display rank. The CPC and the rank may be varied automatically depending on values specified by the advertiser and by other advertisers associated with search listings that include the same search term. For example, the system may automatically reduce the CPC of a listing to a minimum while still maintaining a specified rank. The search engine matches search terms or other portions of the search listings with a search query received from a searcher. The matching search listings are organized according to CPC and display rank and returned to the searcher. If a search listing is referred to the searcher, an economic value of an amount equal to the CPC is payable by the advertiser or owner, who may keep an account for this purpose. A referral of a search listing in this case might be an impression, such as including information about the search listing in the display results, a click through by the searcher, or some post-click through action by the searcher. This embodiment is exemplary only. The notification method may be applied to other types of database search systems as well for advising owners or others associated with listings in a database of a changed condition of a search listing.

In accordance with the present embodiment, each advertiser can create a new Automatic Flight Management function by specifying: 1) the terms {T₁, T₂, . . . , T_(k)} to be bid on for the flight, 2) the budget B for the flight, 3) the duration I of the flight, 4) the conversion rate R, and 5) the average profit/action P. The flight duration may extend across multiple days, and the advertiser can update the conversion rate R and the average profit/action P at any time.

As mentioned earlier, there are two major embodiments of automatic flight management: Optimal Flight Management and Guided Flight Management, with variations that make further efficiency improvements. In each of these embodiments, the system re-computes the optimal bids throughout the flight, to respond to dynamics of the marketplace.

Optimal Flight Management

The procedure Optimal Flight Management optimizes the advertiser's total profit. The procedure initializes the parameters of the flight, and then enters a loop. Each iteration of the loop computes the optimal bids for the terms {T₁, T₂, . . . , T_(k)} that maximize profit. This is accomplished by initializing the flight parameters for each iteration, and then computing the optimal bids. The newly computed bids are then instantiated by setting the bids for the terms in the live marketplace, and the optimal bids are set with price-protection, which maximizes the advertiser's profit by reducing the CPC of the terms, whenever this is possible without negatively impacting the rank of a term. It is possible that it is optimal to not bid on a term, in which case it is given a bid of zero. A report of the current bids is sent to the advertiser for review. The advertiser can manually override any of the bids using DTC, if he so desires.

After setting the bids, the procedure waits for some time period, or a random time, or for some event (e.g., an increase in the number of searchers by the marketplace operator). For example, the system could wait for one day. At the end of this waiting period the system re-computes the remaining budget in the flight and the remaining flight duration. The process repeats itself at the next iteration, re-computing the optimal bids for the remainder of the flight. The iteration stops when the ending time of the flight is reached.

Every term has a list of possible rank/bid combinations. The array ranks&bids, records the possible rank/bid combinations for every term. The value of ranks&bids (T_(i)) is a list of tuples [<rank-a, bid-a>, . . . , <rank-n, bid-n>] for term T_(i). The array index defines which tuple to use for every term. The value of index (T_(i)) is the tuple to user for term T_(i). If index (T_(i))=0, then term T_(i) has a bid of zero (it is not selected). If index (T_(i))=1, then the bid of the first tuple is used for T_(i), and similarly for other values of index (T_(i)).

The function “rank-of” takes as input the list of tuples [<rank-a, bid-a>, . . . , <rank-n, bid-n>] and an index i . It returns the rank of the ith tuple, which is rank-i. Similarly, the function “bid-of” takes as input the list of tuples and an index i. It returns the bid of the ith tuple, which is bid-i.

One embodiment of the procedure optimal flight management is illustrated in FIG. 10. The procedure begins at block 1000. At block 1002, the procedure waits until the beginning of the advertising flight. At block 1004, a procedure initialize flight is called. One embodiment of the procedure initialize flight will be described below in conjunction with FIG. 11.

At block 1006, a looping operation begins. This loop continues until the end of the flight. At block 1008, flight parameters are initialized by calling a procedure set flight parameters. One embodiment of this procedure will be described below in conjunction with FIG. 12. At block 1010, a procedure generate optimal flight is called. One embodiment of this procedure will be described below in conjunction with FIG. 13.

At block 1012, a looping operation begins for all search terms specified by an advertiser. At block 1014, is determined if the index of the current search term is equal to 0. If so, at block 1016, a variable value PP-BID for the current such term is set equal to a value 0. At block 1018, a variable value last-rank for the current search term is set equal to the number of ranks for the current search term plus 1.

If, at block 1014, the index for the current search term is not equal to zero, at block 1020, the value of the variable PP-BID for the current search term is set equal to the value of BID-OF (ranks&bids (T), index (T)). The value last rank (T) is set equal to the value of rank-of (ranks&bids (T), index (T)). The looping operation is repeated, block 1024, until all search terms of the advertiser have been processed.

At block 1026, a report is sent to the advertiser. That report shows the current bid that is determined by the procedure. At block 1028, a flag first-execution is set to a value false. At block 1030, some time period, which may be a random time or may depend on some external event, is awaited. At block 1032, the value remaining-budget is set equal to the total budget B minus the budget spent from the start of the flight. At block 1034, the value remaining-flight is set equal to the time interval from the current time until the end of the current interval. At block 1036, the looping operation returns to block 1006 if the ending time for the flight has not been reached. When the flight end has been reached, the process ends at block 1038.

The procedure optimal flight management may also be embodied in accordance with the pseudocode shown below.

Procedure Optimal Flight Management( ) Wait until the beginning of the flight; Initialize Flight; Loop until the end of the flight  Set flight parameters;  Generate Optimal Flight;  For every term T_(i) in {T₁,T₂,...,T_(k)}   If index(T_(i)) = 0    Assign price-protected-bid(T_(i)) = 0;    Assign last-rank(T_(i)) = #ranks(T_(i))+1;   Else    Assign price-protected-bid(T_(i)) =     bid-of(ranks&bids(T_(i)),index(T_(i)));    Assign last-rank(T_(i)) = rank-of(ranks&bids(T_(i)), index(T_(i)));   End If;  End For;  Send report to advertiser;  Assign first-execution = false;  Wait for a random time, or some time period, or for an event;  Assign remaining-budget = B − budget spent from start;  Assign remaining-flight = interval from now to end of I; End Loop; End Procedure;

The procedure Initialize Flight initializes the parameters at the start of automatic flight management. This includes setting the remaining budget, setting the new duration of the flight to start from the current time, recording that this is the first time the bids for the flight will be computed, and initializing the state variables to record that every term is not currently selected (it's “last-rank” value is zero).

FIG. 11 shows a flow diagram illustrating one embodiment of the procedure initialize flight. The procedure begins at block 1100. At block 1102, the value of the variable remaining-budget is set equal to the value of the variable B. At block 1104, the value of the variable remaining-flight is set equal to the value of the variable I. At block 1106, the flag first-execution is set equal to a value true.

A loop begins at block 1108, processing all search terms of the advertiser index by the variable T. At block 1110, the variable last-rank (T) is initialized to a value zero. At block 1112, the looping operation continues, returning control to block 1108. The procedure initialize flight ends at block 1114.

Another embodiment of the procedure initialize flight is illustrated in the pseudocode below.

Procedure Initialize Flight( ) Assign remaining-budget = B; Assign remaining-flight = I; For every term T_(i) in {T₁,T₂,...,T_(k)} Assign last-rank(T_(i)) = 0; End For; End Procedure;

The procedure Set Flight Parameters resets the parameters at the start of every execution of automatic flight management. As mentioned earlier, the optimal bids for the terms are recomputed periodically throughout a flight. Before each computation, the current state of the marketplace and any feedback from the advertiser are used to update the parameters used to compute the optimal bid values.

At any time the advertiser can update the conversion rate of any term at any rank. This can take into account the latest information from the advertiser's web site. The system first computes R_(max), the highest conversion rate, R(T_(i), j), of any term T_(i) in {T₁, T₂, . . . , T_(k)} at any rank j. It next “normalizes” the number of clicks and bid of every term at every rank, so that all terms at all ranks have the same conversion rate R_(max). The normalization involves multiplying the number of clicks of every term T_(i) at every rank j by the factor $\frac{R\left( {T_{i},j} \right)}{R_{\max}},$ and multiplying the bid of every term T_(i) at every rank j, by the factor $\frac{R_{\max}}{R\left( {T_{i},j} \right)}.$

The procedure historical-clicks uses historical data from the marketplace to estimate the number of clicks for a given term and rank over a time period. The procedure current-bid returns the current bid of a term at a rank.

FIG. 12 is a flow diagram illustrating one embodiment of the procedure set flight parameters. The procedure begins at block 1200. At block 1202, the variable R_(max), corresponding to the highest conversion rate, is set equal to the maximum conversion rate for any search term T at any rank J. At block 1204, the variable L is set equal to the length of the remaining flight.

A looping operation begins at block 1206, looping over all search terms T and all ranks J. At block 1208, the conversion-rate for the current term and rank is set equal to the maximum conversion rate, R_(max). At block 1210, the variable clicks for the current search term and rank is set equal to the ratio of the conversion rate for the current term and rank to the maximum conversion rank multiplied by the total number of clicks for the current search term, rank and the current remaining length of the flight. At block 1212, the current bid for the search term T at rank J is set equal to the ratio of the maximum conversion rate to the current conversion rate for the search term T at rank J multiplied by the current bid for the search term T at the current rank J. At block 1214, the looping operation returns control to block 1206 until all search terms at every rank have been processed. The procedure set flight parameters ends at block 1216.

A second embodiment of the procedure set flight parameters is illustrated in the pseudocode below.

Procedure Set Flight Parameters ( ) Assign R_(max) = maximum R(T_(i), j) of any term T_(i) at any rank j; Assign L = length of remaining-flight; For every term T_(i) in {T₁,T₂,...,T_(k)} at every rank j Assign conversion-rate(T_(i), j) = R_(max); ${{{Assign}\quad{{clicks}\left( {T_{i},j} \right)}} = {\frac{R\left( {T_{i},j} \right)}{R_{\max}}*{historical}\text{-}{{clicks}\left( {T_{i},j,L} \right)}}};$ ${{{Assign}\quad{{bid}\left( {T_{i},j} \right)}} = {\frac{C_{R,\max}}{C_{R,i,j}}*{current}\text{-}{{bid}\left( {T_{i},j} \right)}}};$ End Procedure;

The procedure Generate Optimal Flight computes the optimal bids for the terms {T₁, T₂, . . . , T_(k)} to maximize the advertiser's total profit. The procedure first initializes the calculation parameters—these keep track of the best bids found so far, and the possible ranks that the advertiser can be bid at for each term, and the minimum bids for these ranks.

The main body of the procedure is a loop. Each cycle of the loop examines a new combination of bids for the terms. If the current combination is the best so far, then it is recorded as the new best combination. When all combinations have been examined, the procedure terminates and returns the best combination found.

Each cycle of the loop first examines the current combination of bids for every term {T₁, T₂, . . . , T_(k)}. It computes the total number of clicks, the average bid, the total cost, and the total advertiser profit for the current combination. The current combination is ignored if its average bid is greater than the maximum average CPC specified by the advertiser (C), or the total cost is greater than the remaining budget. Otherwise, the current combination is compared to the previous best combination. If the current combination is better, then it replaces the previous best combination with the current combination. The last step of the loop picks a new combination of bids for the terms {T₁, T₂, . . . , T_(k)}. The procedure exits and returns the best combination of bids when it has finished examining all combinations.

FIG. 13 is a flow diagram illustrating one embodiment of the procedure generate optimal flight. The procedure begins at block 1300. At block 1302, a procedure initialize calculation parameters is called. One embodiment of this procedure will be described below in conjunction with FIG. 15.

At block 1304, a looping operation is entered. At block 1306, the values of the variables clicks/bid/cost/profit is set equal to the resulting values returned by a procedure current combination. One embodiment of this procedure will be described below in conjunction with FIG. 17. At block 1308, a comparison is made to determine if the current bid is less than or equal to the maximum cost per click specified by the advertiser and if the cost of the flight is less than or equal to the remaining budget. If not, control proceeds to block 1318. Otherwise, at block 1310, the comparison is made to determine if the current combination is better than the previous best combination. A procedure better is called to perform this operation. One embodiment of the procedure better will be described below in conjunction with FIG. 14. If a comparison of block 1310 does not produce a true result, control proceeds to block 1318. Otherwise, at block 1312, the value of the variable best-index is set equal to the index of the current combination. Similarly, at block 1314, the value of the variable best-clicks is set equal to the value of the variable clicks for the current combination and at block 1316, the value of the variable best-profit is set equal to the value of the variable profit for the current combination.

At block 1318, it is determined if all combinations have been examined. If not, the value of the variable index is incremented at block 1324 and at block 1326 the looping operation returns to block 1304 to process another combination. If, at block 1318, all combinations have been considered, at block 1320, the index of the best combination of terms and bids is returned by the procedure. The procedure ends at block 1322.

A second embodiment of the procedure generate optimal flight is illustrated by the pseudocode below.

Procedure Generate Optimal Flight(Terms) Initialize Calculation Parameters; Loop  Assign clicks/bid/cost/profit = current combination(index);  If bid <= C and cost <= remaining-budget   If best-index = { } or better(clicks,profit)    Assign best-index = index;    Assign best-clicks = clicks;    Assign best-profit = profit;   End If;  End If;  If max-index?(index) return best-index;  Assign index = increment-index(index, T₁); End Loop; End Procedure;

The procedure Better checks if the current combination is better than the best combination seen in the past. The advertiser can specify the average profit/action P for searchers transferred to the advertiser's web site

If the advertiser has not specified the average profit/action (the value of P is minus one), then the current combination is better if it has more clicks. Otherwise, the current combination is better if it leads to a higher total profit for the advertiser.

One embodiment of the procedure better is illustrated in flow chart of FIG. 14. The procedure begins at block 1400. At block 1402, the value of the variable P, corresponding to the average profits per action is compared with the value −1. If P is equal to −1, at block 1404, it is determined if the value of the variable clicks is greater than the value of the variable best-clicks. If not, at block 1406, the procedure returns the logical value false. If clicks is greater than best-clicks, at block 1408, the procedure returns the logical value true. If, at block 1402, P is not equal to −1, at block 1410, it is determined if the value of the variable profit is greater than the value of the variable best-profit. If not, at block 1412, the procedure returns the logical value false. If profit is greater than best-profit at block 1410, at block 1408, the procedure returns the logical value true. The procedure ends at block 1414.

An alternative embodiment of the procedure better is shown in the pseudocode below.

Procedure Better(clicks,profit) If P = −1 If clicks > best-clicks return True Else return False; Else If profit > best-profit return True Else return False; End If; End Procedure;

The procedure Initialize Calculations resets the parameters at the start of computing the optimal bids. First, it records that no best combination of bids (best-index) has been found so far, and that no clicks and profit have been recorded for any combination. The second step is to compute the possible ranks, and the minimum bids for these possible ranks, for every term {T₁, T₂, . . . , T_(k)}. It also records the number of possible ranks that the advertiser can bid on (#ranks). The last step sets the current combination to have a zero bid for every term (index (T_(i)) is set to zero for every term).

FIG. 15 is a flow diagram illustrating one embodiment of a procedure initialize calculation parameters. The procedure begins at block 1500. At block 1502, the variable best-index is set to an empty value. At block 1504 the variable best-clicks is initialized to 0. At block 1506, the variable best-profit is initialized to 0 as well. A looping operation begins at block 1508. At block 1510, the variable ranks&bids for the current search term is set equal to the value returned by a procedure possible-ranks&bids. One embodiment of the procedure possible-ranks&bids is shown below in conjunction with FIG. 16. At block 1512, the variable #ranks for the current search term is set equal to the length of the array ranks&bids for the current search term. At block 1514, the contents of the index array for this current search term is initialized to 0. At block 1516, the looping operation ends and control is returned to block 1508 until all search terms have been processed. The procedure ends at block 1518.

Another embodiment of the procedure initialize calculation parameters is shown in the pseudocode below.

Procedure Initialize Calculation Parameters( ) Assign best-index = { }; Assign best-clicks = 0; Assign best-profit = 0; For each term T_(i) in {T₁,T₂,...,T_(k)}  Assign ranks&bids(T_(i)) = possible-ranks&bids(T_(i));  Assign #ranks(T_(i)) = length(ranks&bids(T_(i)));  Assign index(T_(i)) = 0; End For; End Procedure;

The procedure Possible Ranks & Bids computes the possible ranks that a new advertiser can be at for a term T_(i), and the minimum bids for these ranks. The term T_(i) has some existing bids in the marketplace for rank 1 to some maximum number of ranks r. It is always possible to be at rank r+1 by selecting the minimum bid. It is also possible to be at rank 1 by selecting the current bid for rank 1 plus $0.01. It may or may not be possible to be at other ranks in between rank 1 and rank r. For example, if the current listings at rank 3 and rank 4 have a bid of $0.35, then it is not possible for a new advertiser to be at rank 4. The new advertiser can only be at rank 3 or rank 5, because all listings with the same bid are ordered chronologically. For any two listings with the same bid, the one with the earlier time at which its bid was set has a better rank.

The procedure starts by finding the existing bids for every rank. These existing bids are then sorted from minimum bid (for the worst rank) to the maximum bid (for rank 1). The variable “worst-rank” records the number of the worst rank. The procedure next initializes the loop variables “current-rank”, “current-bid”, and “ans” (the answer).

The procedure loops through every existing bid, starting from the second-lowest bid (rest (bids)) to the highest bid. At each iteration of the loop, the system checks if the bid for a rank is the same as the bid for the next worse rank. If it is not, then there is “room” to add the new advertiser at the next worse rank with a bid of one cent over the current bid for the next worse rank. At the end of each iteration, the procedure sets the current rank to be one rank better than the current rank, and assigns “bid” to the bid of the current rank.

The procedure returns the list of possible ranks, and the minimum bid to be at this rank. Each element of the list is a tuple: <rank, bid>. This list of tuples is sorted from the lowest bid to the highest bid.

FIG. 16 is a flow diagram illustrating one embodiment of a procedure possible ranks&bids. The procedure begins at block 1600. At block 1602 the value of the variable bids is set equal to the set of bids for the current search term T at all ranks. At block 1604, the bids are sorted from lowest to highest bid. At block 1606, the variable worst-rank is set equal to the rank of the lowest rank bid. At block 1608, the variable current-rank is initialized to the value of worst-rank. At block 1610, the variable current-bid is initialized to the first entry in the array bids. At block 1612, the variable is initialized to the pair of values: the value of worst-rank plus one, and the minimum bid.

A looping operation begins at block 1614, using the variable x over the set of other bids. At block 1616, the variable x is compared with the value of the current bid. If x is equal to the current-bid, control proceeds to block 1620. If x is not equal to the current-bid, at block 1618 the pair of values current-rank and current-bid plus the minimum possible bid value are added to the end of the array ans. At block 1620, the value of the variable current-bid is set equal to the value of x. At block 1622, the value of the variable current-rank is decremented by 1. The looping operation ends at block 1624 and control is returned to block 1614, unless the highest bid has been reached.

At block 1626, an entry having a pair of values consisting of rank 1 and a bid equal to the current bid plus the minimum bid amount is added to the array ans. At block 1628, the procedure returns the array ans. The procedure ends at block 1630.

A second embodiment of the procedure possible ranks&bids is shown in accordance with the pseudocode.

Procedure Possible Ranks & Bids(T_(i)) Assign bids = the set of bid(T_(i), j) for all ranks j; Assign bids = sort bids from lowest to highest bid; Assign worst-rank = length (bids); Assign current-rank = worst-rank; Assign current-bid = first(bids); Assign ans = [<worst-rank + 1,min-bid>]; Loop x over rest(bids);  If x <> current-bid   Add <current-rank,current-bid + $0.01> to end of ans;  End If;  Assign current-bid = x;  Assign current-rank = current-rank − 1; End Loop; Add <1,current-bid + $0.01> to end of ans; Return ans; End Procedure;

The procedure Current Combination takes as input the current combination, which is the bid/rank combination for every term {T₁, T₂, . . . , T_(k)}, and computes its: 1) total number of clicks, 2) average bid, 3) total cost, and 4) total advertiser profit. The total number of clicks is the sum of the clicks for each term at its selected rank over the remaining flight duration. The total cost is the sum of the costs of each of the terms at its selected rank over the remaining flight duration. The average bid is the total cost divided by the total number of clicks. The total advertiser profit is the sum of the profit for each term.

The previous procedure Possible Ranks & Bids calculates the list of possible <rank, bid> tuples for a term. The procedure Initialize Calculation Parameters initialized ranks&bids (T_(i)) to the list of possible <rank, bid> tuples for term T_(i). The “current combination” being considered is defined by the selected <rank, bid> tuple for each term. The tuple selected for term T_(i) is defined by index(T_(i)). If index (T_(i))=0, then term T_(i) has a bid of zero (it is not selected in the current combination). If index (T_(i))=1, then the first tuple ranks&bids(1) defines the bid and rank for term T_(i), and similarly for other values of index(T_(i)). Recall that the list of tuples are sorted by increasing bid, so the first tuple has the lowest bid, which corresponds to the worst rank.

At iteration i the procedure extracts the current rank and bid of term T_(i). The number of clicks of T_(i) at its current rank, over the remaining flight time (L), is estimated from the historical marketplace data. The cost for T_(i) is the number of clicks times the bid. The profit for T_(i) is the product of: 1) number of clicks and 2) the profit/action times the conversion rate, minus the bid. At the end of the iteration the total cost is incremented by the cost for T_(i), the total clicks is incremented by the clicks for T_(i), and the total profit is incremented by the profit for T_(i).

The loop terminates when all terms T_(i) in {T₁, T₂, . . . , T_(k)} have been considered. The procedure finally returns four values: 1) the total clicks, 2) the average bid, 3) the total cost, and 4) total profit for the terms {T₁, T₂, . . . , T_(k)}.

FIG. 17 is a block diagram showing one embodiment of the procedure current combination. The procedure begins at block 1700. At block 1702, the variable cost is initialized to a value of 0. At block 1704, the variable clicks is initialized to a value of 0. At block 1706, the variable profit is initialized to a value of 0.

At block 1708, a looping operation begins, using search terms of the advertiser as the looping index. At block 1710, it is determined if the index value for the current search term is not equal to 0. If the index for the current search term is equal to 0, control proceeds to block 1724. Otherwise, at block 1712, the variable rank is assigned the rank of the index(T) element of the array ranks&bids for the search term T. At block 1714, the variable bid is assigned the bid of the index(T) element of the array ranks&bids for the search term T. At block 1716, the number of clicks for the search term T at the current rank is estimated. At block 1718, the current cost value is set equal to the sum of the cost and the product of the bid and number of clicks for the search term T. At block 1720, the number of clicks is incremented by the number of clicks for the search term T. Finally, at block 1722, the profit is incremented by the product of the number of clicks and the profit per action multiplied by the conversion rate, minus the bid for the search term. At block 1724, control returns to block 1708 for another iteration to the loop when the value of the variable index for the current search term is equal to 0, the loop is exited and, at block 1726, the procedure returns the values of the four variable clicks, cost per clicks, cost and profit. The procedure ends at block 1728.

A second embodiment of the procedure current combination is shown in the pseudocode below.

Procedure Current Combination(index) Assign cost = 0; Assign clicks = 0; Assign profit = 0; Loop over each term T_(i) in {T₁,T₂,...,T_(k),}  If index(T_(i)) <> 0   Assign rank(T_(i)) = rank-of(rankS&bids(T_(i)),index(T_(i)));   Assign bid(T_(i)) = bid-of(rankS&bids(T_(i)),index(T_(i)));   Assign clicks(T_(i)) = historical-clicks(T_(i),rank(T_(i)),L);   Assign cost = cost + bid(T_(i))* clicks(T_(i));   Assign clicks = clicks + clicks(T_(i));   Assign profit = profit +    clicks(T_(i))*(P* conversion-rate(T_(i),rank(T_(i))) − bid(T_(i)));  End If; End For; Return clicks, cost/clicks, cost, profit; End Procedure;

The procedure Max-Index? Checks if there are no more combinations to consider. The starting combination has a zero bid for all terms. There are no more combinations if the current combination is at rank 1 for all terms.

As mentioned earlier, the “current combination” is defined by the selected <rank, bid> tuple for each term. The tuple selected for term T_(i) is defined by index (T_(i)). If index (T_(i))=0, then term T_(i) has a bid of zero (it is not selected in the current combination). If index (T_(i))=1, then the first tuple ranks&bids (1) defines the bid and rank for term T_(i), and similarly for other values of index (T_(i)). There are no more combinations if the index for every term is at its maximum value. This corresponds to every term being bid to rank 1 (recall that the list of tuples is sorted from the lowest bid to highest bid—which corresponds to rank 1).

FIG. 18 is a flow diagram illustrating one embodiment of the procedure max-index?. The procedure begins at block 1800. At block 1802, a looping operation begins using the search terms of the advertiser as the looping index. At block 1804, a determination is made whether the value of the variable index for the current search term is less than the number of ranks for the search term. If so, at block 1808, a logical value False is returned and control proceeds to block 1812. Otherwise, at block 1806, the looping operation returns control to block 1802 for processing a next search term. After the loop is exited, at block 1810, the procedure returns a logical value True. The procedure ends at block 1812.

An alternative embodiment of the procedure max-index? is shown in the pseuocode below.

Procedure Max-Index?(index) For each term T_(i) in {T₁,T₂,...,T_(k)} If index(T_(i)) < #ranks(T_(i)) return False; End For; Return True; End Procedure;

The procedure Increment-Index selects the next combination to consider. Each term T_(i) in {T₁, T₂, . . . , T_(k)} has an index value index (T_(i)), which is an index into a list of <rank,bid> tuples. These tuples are sorted in the list from lowest bid to highest bid. The procedure first attempts to increase the index of T₁ (to select the tuple with the next higher bid and next better rank). If the index of T₁ is already at its maximum, then the index of T₁ is set to zero (we are no longer bidding on T₁), and the same process is repeated for T₂. This is similar to incrementing a k-digit number. We first increment one digit. If we reach the maximum for one digit, we set it to zero and increment the next digit, and so on. Here each digit has its own range (0 for not bidding on the term, 1 for the worst rank, 2 for the second worst rank, up to #ranks (T_(i)) for rank 1). The function next takes a term T_(i) as input and returns the next term T_(i+1) in {T₁, T₂, . . . , T_(k)}, i.e., next (T_(i))=T_(i+1).

FIG. 19 is a flow diagram illustrating one embodiment of the procedure increment-index. The procedure begins at block 1900. At block 1902, it is determined if the index value for the current search term is equal to the number of ranks of this current search term. If not, at block 1904, the index value for the search term is incremented by 1. Control then proceeds to block 1912.

If, at block 1902, the index for the search term is equal to the number of ranks for the search term, then, at block 1906, the index for the search term is set to 0 and, at block 1908, it is determined if i and k are equal. If so, control proceeds to block 1912 then the procedure exits. Otherwise, at block 1910, the procedure calls itself recursively to increment the index of the next term. The procedure ends at block 1912.

An alternative embodiment for implementing the procedure increment-index is shown in the pseudocode below.

Procedure Increment-Index(index, T_(i)) If index(T_(i)) = #ranks(T_(i))  Assign index(T_(i)) = 0;  If i = k return;  Increment-index(index, T_(i+1)); Else  Assign index(T_(i)) = index(T_(i)) + 1; End If; End Procedure; Guided Flight Management

The previous procedures have defined the first variation in which the system computes the optimal bids for the terms {T₁, T₂, . . . , T_(k)} to maximize advertiser profit. As mentioned earlier, the cost of finding the optimal solution can be computationally intractable for large problems.

The second variation, called Guided Flight Management, addresses this problem by finding a solution that may be optimal in most situations (though not all), and has greatly reduced computation cost.

The key to the efficiency of Guided Flight Management is that it only considers the combinations that are likely to be in the optimal solution. This greatly reduces the number of combinations that need to be considered. In order to maximize advertiser profit, automatic flight management must set the bids of the terms {T₁, T₂, . . . , T_(k)} to get the most clicks for the budget B. This is equivalent to having the minimum average bid for the term {T₁, T₂, . . . , T_(k)}.

Guided Flight Management starts by looking at the combination with the lowest bid and greatest clicks. This is with the minimum bid for all terms. It then enters a loop—at each iteration increasing the bid to improving the rank of one term in {T₁, T₂, . . . , T_(k)} so as to increase the average bid by the least amount. The iterations are repeated as long as the total cost is less than the flight budget and as long as the average bid is less than the advertiser specified maximum average CPC. At the end, the best combination which spends the flight budget and which has the lowest average bid is returned.

The procedure Guided Flight Management is identical to the procedure Optimal Flight Management (described earlier), except that it uses the procedure Generate Guided Flight, instead of Generate Optimal Flight. This is the line in boldface below. The same descriptions presented earlier apply for the other common procedures.

FIG. 20 is a flow diagram illustrating one embodiment of a guided flight management method. The method begins at block 2000. At block 2002, the operation of the procedure awaits the beginning of the advertising flight. At block 2004, the flight is initialized. In one embodiment, this may be performed in accordance with the method described above in conjunction with FIG. 11.

At block 2006, a looping operation begins which extends throughout the duration of the flight. The first looping operation is block 2008, in which flight parameters are established. This may be performed in accordance with the acts described above in conjunction with FIG. 12. At block 2010, a procedure generate guided flight is initiated. One embodiment of this procedure will be described below in conjunction with FIG. 21.

A looping operation begins at block 2012 during which all search terms T of the advertiser associated with the advertising flight are processed. At block 2014, it is determined if the index of the current search term is equal to 0. If so, at block 2016, a variable PP-BID for the search term is set equal to 0 and at block 2018, a variable last-rank for the search term is set equal to the number of ranks for which there are bids for the search term T, plus 1. Control then proceeds to block 2024 and return to block 2012 for another pass through the loop. Thus, at block 2014, the index of the current search term is not equal to 0, at block 2020, the value of the variable PP-BID for the current search term is set equal to the value of the variable contained in BID-OF (ranks&bids (T), index (t)). At block 2022, the value of the variable last-rank for the current search term is set equal to the rank of the current search term.

After exiting the loop which includes blocks 2012, 2014, 2016, 2018, 2020, 2022, 2024, at block 2026, a report of current bids and ranks as determined by the looping operation is sent to the advertiser associated with the advertising flight. At block 2028, a logical variable First-Execution is set to a value False. At block 2030, the procedure waits for a time before resuming. This time may be determined by a random timing, elapse of a predetermined time period or by the occurrence of an event. At block 2032, a variable remaining-budget is set equal to the difference between the overall budget for the flight, B, and the budget spent from the start of the flight of advertising. At block 2034, the variable remaining-flight is set equal to the difference between the current time and the end of the predetermined flight interval. At block 2036, the end of the loop is reached and control returns to block 2006 for additional processing. Upon termination of the flight, the method of FIG. 20 is terminated at block 2038.

A second embodiment of the procedure guided flight management is shown in the pseudocode below.

Procedure Guided Flight Management( ) Wait until the beginning of the flight; Initialize Flight; Loop until the end of the flight  Set flight parameters;  Generate Guided Flight;  For every term T_(i) in {T₁,T₂,...,T_(k)}   If index(T_(i)) = 0    Assign price-protected-bid(T_(i)) = 0;    Assign last-rank(T_(i)) = #ranks(T_(i))+1;   Else    Assign price-protected-bid(T_(i)) =     bid-of(ranks&bids(T_(i)),index(T_(i)));    Assign last-rank(T_(i)) =     rank-of(ranks&bids(T_(i)),index(T_(i)));   End If;  End For;  Send report to advertiser;  Assign first-execution = false;  Wait for a random time, or some time period, or for an event;  Assign remaining-budget = B − budget spent from start;  Assign remaining-flight = interval from now to end of I; End Loop; End Procedure;

The procedure Generate Guided Flight only explores the combinations that are likely to be in the optimal solution. The procedure starts by initializing the calculation parameters, as described earlier for the procedure Generate Optimal Flight. It next enters a loop. Each iteration of the loop explores a new combination, where the rank of one of the terms is improved by one position—this always results in a higher total cost and higher average bid.

At each iteration the system checks if the current combination is better than the best combination seen so far. The best combination is replaced with the current combination if this is the case.

The loop exits if the current combination has a total cost greater than the flight budget B, or if the average bid of the current combination is greater than the maximum average CPC C, or if there are no more combinations. The procedure returns the best combination that was found.

FIG. 21 is a flow diagram showing one embodiment of the procedure generate guided flight. The procedure begins at block 2100. At block 2102, a procedure initialize calculation parameters is called. One embodiment of this procedure is described above in conjunction with FIG. 15.

At block 2104, a looping operation begins. At block 2106, the values of the four variables clicks/bid/cost/profit is set equal to the values returned by the procedure current combination. One embodiment of this procedure is described above in conjunction with FIG. 17. At block 2108, it is determined if the current bid is greater than the maximum CPC C or if the cost is greater than the variable remaining-budget or if the procedure max-index? returns the logical value True. One embodiment of the procedure max-index? is described above in conjunction with FIG. 18. If the text of block 2108 is true, at block 2110 the procedure returns the value of the variable best index and the procedure ends at block 2112. Otherwise, at block 2114, it is determined if the variable best-index stores an empty value or if the value returned by the procedure better has a true value. One embodiment of the procedure better is described above in conjunction with FIG. 14. If the test of block 2114 is true, at block 2116, the value of the variable best-index is set equal to the value of the variable index. At block 2118, the value of the variable best-clicks is set equal to the value of the variable clicks. At block 2120, the value of the variable best-profit is set equal to the value of the variable profit. If the test of block 2114 is not true or after processing block 2120, at block 2112 the value of the variable index is set equal to the value returned by the procedure minimum-bid-extend. One embodiment of this procedure is described below in conjunction with FIG. 22. The looping procedure ends at block 2124, and control is returned to block 2104.

An alternative embodiment of the procedure generate guided flight is shown in the pseudocode below.

Procedure Generate Guided Flight(Terms) Initialize Calculation Parameters; Loop  Assign clicks/bid/cost/profit = current combination(index);  If bid > C or cost > remaining-budget or max-index?(index)   Return best-index;  End If;  If best-index = { } or better(clicks,profit)   Assign best-index = index;   Assign best-clicks = clicks;   Assign best-profit = profit;  End If;  Assign index = minimum-bid-extend(index); End Loop; End Procedure;

The main difference between Guided Flight Management and Optimal Flight Management Is in generating the next “current combination.” For Optimal Flight Management we consider all combinations. For Guided Flight Management we look at the current combination, and see how to improve the rank of one of the terms, so that the average bid is increased by the least amount. This is accomplished by the procedure Minimum Bid Extend.

The current combination is defined by the rank and bid of every term {T₁, T₂, . . . , T_(k)}. Each term T_(i) has a list of of <rank,bid> tuples, sorted from lowest bid to highest bid. The tuple selected for T_(i) is defined by the value of index (T_(i)). If this is zero then the bid is zero for T_(i). If this is one, then this the first rank/bid combination (minimum bid and worst rank).

The procedure Minimum Bid Extend starts by initializing the variables “lowest-bid-index” (the index of the combination explored that results in the lowest average bid) and “lowest-bid” (the average bid of the combination explored that results in the lowest average bid).

The procedure next enters a loop, where each iteration of the loop considers improving the rank of one of the terms {T₁, T₂, . . . , T_(k)}. This is accomplished by incrementing the index of the selected term by one. Each iteration computes the total clicks, average bid, total cost, and total profit for the new combination being considered. If the average bid of the current combination is less than the best average bid considered on any previous iteration, then the best extension is replaced with the current combination.

The procedure exits the loop when all extensions have been considered, and it returns the new combination that increments the average bid by the least amount.

FIG. 22 is a flow chart illustrating one embodiment of a procedure minimum bid extend. The procedure begins at block 2200. At block 2202, the variable lowest-bid-index is initialized to 0 and at block 2204, the variable lowest-bid is initialized to 0.

A looping operation begins at block 2206, looping over all search terms of the advertiser. At block 2208, it is determined if the index of a current search term is less than the number of ranks of the search term. If not, the end of the loop is reached and control proceeds to block 2222. Otherwise, at block 2210, the value of the variable index is incremented by 1 and at block 2212, the value of the four variables clicks/bid/cost/profit is set equal to the four values returned by the procedure current-combination. One embodiment of this procedure is described above in conjunction with FIG. 17.

At block 2214, it is determined if the value of the variable lowest-bid-index is equal to 0 or if the current bid is less than the value of the variable lowest-bid. If not, control proceeds to block 2220. Otherwise, at block 2216, the variable lowest-bid-index is set equal to the current value of the variable index, at block 2218 the variable lowest-bid is set equal to the current bid for the current search term, and at block 2220 the index for the current search term is decremented by 1. At block 2222, the loop returns control to block 2206. When the loop ends, after all search terms of the advertiser has been processed, at block 2224, the variable index is set equal to the valuable lowest-bid-index and the procedure ends at block 2226.

An alternative embodiment of the procedure minimum bid extend is shown in the pseudocode below.

Procedure Minimum Bid Extend(Index) Assign lowest-bid-index = 0; Assign lowest-bid = 0; Loop over each term T_(i) in {T₁,T₂,...,T_(k)}  If index(T_(i)) < #ranks(T_(i))   Assign index(T_(i)) = index(T_(i)) + 1;   Assign clicks/bid/cost/profit = current-combination(index);   If lowest-bid-index = 0 or bid < lowest-bid    Assign lowest-bid-index = index;    Assign lowest-bid = bid;   End If;   Assign index(T_(i)) = index(T_(i)) − 1;  End If; End Loop; Assign index = lowest-bid-index; End Procedure; Optimizations

In a third variation of automatic flight management, the system further improves the performance by discarding ranks of the terms {T₁, T₂, . . . , T_(k)} that are not likely to be a part of the optimal solution. By reducing the number of ranks, the combination of ranks to consider is reduced exponentially. This results in greatly improved performance. These optimizations can be applied to both Optimal Flight Management and Guided Flight Management.

There is tradeoff in the number of ranks to discard for each term. If more ranks are discarded then the improvement in performance is exponentially greater, however, if too many ranks are discarded then the optimal solution may be missed. The marketplace operator can control the optimization parameters to make the best tradeoff.

The optimizations are realized by replacing the previously defined procedure Initialize Calculation Parameters with the procedure Initialize Filtered Calculation Parameters.

There are two changes in the new procedure (shown in boldface). First, before the initial execution of automatic flight management, the system estimates the bid b_(o), which if assigned to all terms {T₁, T₂, . . . , T_(k)} results in spending the flight budget B. This estimated optimal bid b_(o) is used to select a band of ranks to consider for every term. The procedure Estimate Original Bids performs this computation.

The second change is that the result of finding the possible ranks and bids for all the terms {T₁, T₂, . . . , T_(k)} is filtered to only consider some of the ranks of each term. This is accomplished by the procedure Filter.

FIG. 23 is a flow diagram illustrating one embodiment of the procedure initialize filtered calculation parameters. The procedure begins at block 2300. At block 2302, the variable best-index is initialized to a value 0. Similarly, at block 2304 and block 2306, the variables best-clicks and best-profit are initialized to the value 0. The test is performed at block 2308 to determine if the variable first-execution is a logical value True. If so, at block 2310, a procedure estimate original bids is called. One embodiment of this procedure will be described below in conjunction with FIG. 24. At block 2312, a loop is entered. At block 2314, the value of the array variable ranks&bids at the index of the current term T is set equal to the value returned by the procedure filter. One embodiment of this procedure will be described below in conjunction with FIG. 27. At block 2316, the array variable #ranks at the index of the current term T is set equal to the length of the array value for the current search term. At block 2318, the index for the current search term is set equal to 0 and at block 2320 the loop ends. Control is returned to block 2312 until all search terms have been processed. After processing all search terms, the procedure ends at block 2322.

An alternative embodiment of the procedure initialize filtered calculation parameters is in illustrated in the pseudocode.

Procedure Initialize Filtered Calculation Parameters( ) Assign best-index = { }; Assign best-clicks = 0; Assign best-profit = 0; If first-execution = true  Estimate original bids; End If; For each term T_(i) in {T₁,T₂,...,T_(k)}  Assign ranks&bids(T_(i)) = filter(T_(i),possible-ranks&bids(T_(i)));  Assign #ranks(T_(i)) = length(ranks&bids(T_(i)));  Assign index(T_(i)) = 0; End For; End Procedure;

The procedure Estimate Original Bids computes the bid b_(o), which if assigned to all terms {T₁, T₂, . . . , T_(k)} results in spending the flight budget B. The variable “best-under” is the highest value of b_(o) that has been found, which results in spending less than the budget B. Similarly, the variable “best-over” is the lowest value of b_(o) that has been found, which results in spending more than the budget B. Initially best-under is zero, best-over is the highest bid to be at rank 1 for any term {T₁, T₂, . . . , T_(k)}, and the initial estimate of b_(o), is “best-over” divided by two.

The procedure loops through a number of iterations, where each iteration adjusts the current bid (b_(o)) so that the total cost gets closer to the flight budget B. The loop terminates if the current value of “bid” results in spending approximately the budget B. The window of approximation is defined by the variable δ, which must be between zero and one. If δ is 0, then the total cost must be exactly B, and if δ is 1, then any total cost less than 2×B is acceptable.

If the total cost with all terms {T₁, T₂, . . . , T_(k)} having the bid “bid” is greater than the budget ((1+δ)×B<cost), then “bid” is adjusted to be halfway in between its current value and “best-under.” Also, if the current “bid” is less than “best-over,” then “best-over” is replaced with “bid.”

Similarly, if the total cost with all terms {(T₁, T₂, . . . , T_(k)} having the bid “bid” is less than the budget ((1−δ)×B>cost), then “bid” is adjusted to be halfway in between its current value and “best-over.” Also, if the current “bid” is greater than “best-under,” then “best-under” is replaced with “bid.”

Each iteration of the loop brings the estimated “bid” closer to the average optimal bid b_(o), which results in spending the budget B. Eventually the value of “bid” will be close enough, and the procedure will terminate and assign this “bid” to original-bid-estimate.

FIG. 24 is a flow diagram illustrating one embodiment of the procedure estimate original bids. The procedure begins at block 2400. At block 2402, the variable best-under is initialized to a value 0. At block 2404, the variable best-over is initialized to be the maximum bid to be at rank 1 for any search term of the advertiser. At block 2406, the variable bid is initialized to one-half the value of the variable best-over.

A loop is under that block 2408. At block 2410, the variable cost is set equal to the result returned by procedure cost with bid. One embodiment of this procedure will be described below in conjunction with FIG. 25. At block 2412, a test is performed to determine if the product of the budget B and the sum of the variable delta and 1 is greater than or equal to the value of the variable cost, and if the variable cost is greater than or equal to the product of the budget B and 1 minus the value of the variable delta. If so, at block 2414, the variable original-bid-estimate is set equal to the current value of the variable bid and the procedure ends at block 2416. Otherwise, at block 2418, it is determined if the product of the budget B and 1 plus the variable delta is less than the value of the variable cost.

At block 2420, it is determined if the value of the variable bid is less than the variable best-over. If so, at block 2422, the value of the variable best-over is set equal to the value of the variable bid. At block 2424, the value of the variable bid is set equal to the floor of the sum of the variable bid and the variable best-under divided by 2. Control then proceeds to block 2434. Otherwise, at block 2426, it is determined if the product of the budget B and 1 minus the variable delta is greater than the variable cost. If so, at block 2428, it is determined if the variable bid is greater than the variable best-under. If so, at block 2430, the variable best-under is set equal to the current value of the variable bid and, at block 2432, the variable bid is set equal to the floor of the sum of the variables bid and best-over divided by 2. Control then proceeds to block 2434 which is the end of the loop.

A second embodiment of the procedure Estimate Original Bids is shown in accordance with the pseudo code below.

Procedure Estimate Original Bids( ) Assign best-under = 0; Assign best-over = max(bid(T₁,1),bid(T₂,1),...,bid(T_(k),1)); Assign bid = best-over/2; Loop  Assign cost = cost with bid(bid);  If (1+δ)×B >= cost >= (1−δ)×B   Exit Loop;  Else If (1+δ)×B < cost   If bid < best-over    Assign best-over = bid;   End If;   Assign bid = floor((bid + best-under)/2);  Else If (1−δ)×B > cost   If bid > best-under    Assign best-under = bid;   End If;   Assign bid = floor((bid + best-over)/2);  End If; End Loop; Assign original-bid-estimate = bid; End Procedure;

The procedure Cost With Bid computes the cost to the advertiser, if all terms {T₁, T₂, . . . , T_(k)} have the bid “bid.” It does this by summing the costs of each individual term with this bid.

FIG. 25 is a flow diagram illustrating one embodiment of the procedure Cost With Bid. The procedure begins at block 2500. At block 2502, the variable total-cost is initialized to a value of zero. The loop begins at block 2504. At block 2506, the variable total-cost is incremented by the result returned by the procedure Term Cost. One embodiment of the procedure Term Cost will be described below in conjunction with FIG. 26. The end of the loop is reached at block 2508, and the loop is repeated until all such terms associated with the advertiser have been processed. At block 2510, the procedure returns the value of the variable total-cost. The procedure ends at block 2512.

An alternative embodiment of the procedure Cost With Bid is illustrated in accordance with the pseudo code below.

Procedure Cost with Bid(bid) Assign total-cost = 0; For each term T_(i) in {T₁,T₂,....,T_(k)}  Assign total-cost = total-cost + term cost(T_(i),bid); End For; Return total-cost; End Procedure;

The procedure Term Cost takes as input a term T_(i) in {T₁, T₂, . . . , T_(k)} and a bid. It computes the cost to the advertiser of bidding at “bid” for T_(i). The procedure first finds all the possible ranks that an advertiser can be at, and the minimum bid for these ranks. It next loops over all possible rank/bids and selects the rank/bid which has the bid closest to the input “bid.” The cost for this rank is the bid times the historical clicks in the marketplace for this rank.

FIG. 26 is a flow diagram illustrating one embodiment of the procedure Term Cost. The procedure begins at block 2600. Several variables are initialized, including the variable closest-bid initialized to the value zero, block 2602, the variable closest-rank initialized to the value zero, block 2604, the variable closest-distance initialized to the value minus one, block 2606, and the variable ranks/bids is set equal to the value returned by the procedure possible-ranks&bids. One embodiment of the procedure Possible-Ranks&Bids is described above in conjunction with FIG. 16.

At block 2610, a looping operation begins using a looping variable x having values between one and the length of the variable ranks/bids. At block 2612, the value of the variable current bid is set equal to the bid for entry X in the listing of ranks/bids. At block 2614, the value of the variable distance is set equal to the absolute value of the difference between the variable bid and the variable current-bid. At block 2616, a test is performed to determine if the value of the variable closest-distance is equal to minus one or if the value of the variable distance is less than the value of the variable closest-distance. If neither condition is true, control proceeds to the end of the loop, block 2622. If either condition of block 2616 is true, at block 2618, the variable closest bid is assigned a value equal to the current-bid. At block 2620, the variable closest-rank is set equal to the rank of the Xth entry of the array rank/bids. At block 2622, looping control returns to block 2610.

After the loop is exited, at block 2624, the variable closest-clicks is set equal to the value returned by a procedure Historical-Clicks. At block 2626, the cost is set equal to the product of variables closest-clicks and closest-bid. At block 2628, the value of the variable cost is returned by the procedure. The procedure ends at block 2630.

An alternative embodiment of the procedure Term Cost is illustrated by the pseudo code below.

Procedure Term Cost(T_(i),bid) Assign closest-bid = 0; Assign closest-rank = 0; Assign closest-distance = −1; Assign ranks/bids = possible-ranks&bids(T_(i)); Loop x from 1 to length(ranks/bids)  Assign current-bid = bid-of(ranks/bids,x);  Assign distance = abs(bid − current-bid);  If closest-distance = −1 or distance < closest-distance   Assign closest-bid = current-bid;   Assign closest-rank = rank-of(rank/bids,x);  End If; End Loop; Assign closest-clicks = historical-clicks(T_(i),closest-rank,L); Assign cost = closest-clicks * closest-bid; Return cost; End Procedure;

The procedure Filter takes as input a term T_(i) and a list of <rank, bid> tuples. The procedure filters some of the tuples, thus removing the ranks of the eliminated tuples of T_(i) from consideration. This filtering is controlled by information provided by the advertiser and/or the marketplace operator.

There are three types of filtering, and one or more of these can be applied in any combination. The advertiser can specify that he wishes to eliminate all ranks of every term, if the rank has fewer clicks than some threshold. The advertiser constraint of the minimum clicks is specified by the value of min-clicks (T_(i)).

An advertiser can specify a range of ranks to consider—from a best rank to a worst rank (inclusive). The advertiser constraint for the worst rank of T_(i) is defined by worst-rank (T_(i)), and the advertiser constraint for the best rank of T_(i) is defined by best-rank(T_(i)).

Auto-filter is the third type of filtering, in which the marketplace operator can use the results of the previous execution of automatic flight management to eliminate ranks that are not close to the ranks of the previous execution for each term {T₁, T₂, . . . , T_(k)}.

FIG. 27 is a flow diagram illustrating one embodiment of the procedure Filter. The procedure begins at block 2700. At block 2702, it is determined if the value of the variable min-clicks for the current search term is not equal to zero. If so, at block 2704, the value of the array ranks/bids is set equal to the result returned by the procedure Filter Min Clicks. One embodiment of this procedure is described below in conjunction with FIG. 28. At block 2706, it is determined if either the best rank for the search term T is greater than one or if the worst rank for the search term is a value other than zero. If so, at block 2708, the contents of the array ranks/bids is set equal to the result returned by the procedure Filter Ranks. One embodiment of this procedure is described below in conjunction with FIG. 29. At block 2710, it is determined if the result returned by the procedure Auto-Filter-Ranks is a logical value true. One embodiment of this procedure is described below in conjunction with FIG. 32. If the test of block 2710 is true, at block 2712, the value of the array ranks/bids is set equal to the result returned by the procedure Auto-Filter. One embodiment of this procedure is described below in conjunction with FIG. 30. At block 2714, the procedure returns the value of the array ranks/bids. The procedure ends at block 2716.

An alternative embodiment of the procedure Filter is shown in conjunction with the pseudo code below.

Procedure Filter(T_(i),ranks/bids) If min-clicks(T_(i)) <> 0  Assign ranks/bids = filter min-clicks(T_(i),ranks/bids); End If; If best-rank(T_(i)) > 1 or worst-rank(T_(i)) <> 0  Assign ranks/bids = filter ranks(T_(i),ranks/bids); End If; If auto-filter-ranks = true;  Assign ranks/bids = auto-filter(T_(i),ranks/bids); End If; Return ranks/bids; End Procedure;

The procedure Filter Min Clicks takes as input a term T_(i) and a list of <rank, bid> tuples. It eliminates all tuples whose ranks have fewer clicks than the click threshold specified by the advertiser for the term T_(i).

FIG. 28 is a flow diagram illustrating one embodiment of a procedure Filter Min Clicks. The procedure begins at block 2800. At block 2802, the variable min is set equal to the value returned by the procedure Min-Clicks. A looping operation begins at block 2804, looping over the variable x extending from the value one to a value equal to the length of the array ranks/bids. At block 2806, the variable rank is set equal to the rank of entry x of the array ranks/bids. At block 2808, the variable clicks is set equal to the value returned by the procedure Historical-Clicks. At block 2810, a test is performed to determine if the value of the variable clicks is less than the variable min. If so, at block 2812, the Xth entry of the array ranks/bids is deleted from the array. Control of the loop returns, block 2814, to block 2804. After the loop has been completely processed, at block 2816, the value of the array ranks/bids is returned by the procedure. The procedure ends at block 2818.

An alternative embodiment of the procedure Filter-Min-Clicks is illustrated in accordance with the pseudo code shown below.

Procedure Filter Min Clicks(T_(i),ranks/bids) Assign min = min-clicks(T_(i)); Loop x from 1 to length(ranks/bids)  Assign rank = rank-of(ranks/bids,x);  Assign clicks = historical-clicks(T_(i),rank,L);  If clicks < min   delete ranks/bids(x) from ranks/bids;  End If; End Loop; Return ranks/bids; End Procedure;

The procedure Filter Ranks takes as input a term T_(i) and a list of <rank, bid> tuples. It eliminates all tuples whose rank is greater than the worst-rank specified by the advertiser or whose rank is less than the best-rank specified by the advertiser.

FIG. 29 is a flow diagram illustrating one embodiment of a procedure Filter Ranks. The procedure begins at block 2900. At block 2902, the variable best is assigned the value of the best-rank of any search term of the advertiser. At block 2904, the variable worst is assigned the worst rank of any such term of the advertiser. A looping operation begins at block 2906, looping over the variable x from a value one to a value equal to the length of the array ranks/bids. At block 2908, the variable rank is set equal to the rank of the Xth entry of the array ranks/bids. At block 2910, a test is performed to determine if the variable rank is less than the value of the variable best or if the variable worst is not equal to zero and the variable rank is greater than the value of worst. If so, at block 2912, the Xth entry of the array ranks/bids is deleted from the array ranks/bids. At block 2914, control returns to the start of the loop, block 2906. At block 2916, the procedure returns the value of the array ranks/bids. The procedure ends at block 2918.

A second embodiment of the procedure Filter Ranks is illustrated in accordance with the pseudo code below.

Procedure Filter Ranks(T_(i),ranks/bids) Assign best = best-rank(T_(i)); Assign worst = worst-rank(T_(i)); Loop x from 1 to length(ranks/bids)  Assign rank = rank-of(ranks/bids,x);  If rank < best or (worst <> 0 and rank > worst)   delete ranks/bids(x) from ranks/bids;  End If; End Loop; Return ranks/bids; End Procedure;

The procedure Auto Filter takes as input a term T_(i) and a list of <rank, bid> tuples. It eliminates all tuples whose rank is outside the range of ranks in which the optimal solution is likely to exist. The filtering is controlled by parameters that can be adjusted by the marketplace operator.

There are two types of auto filters. The first filters the ranks based on their bids—ranks whose bid is far from the bids of the previous execution of automatic flight management are discarded. If this is the very first execution of automatic flight management, the system uses the estimate of the average optimal bid b_(o), which is computed by the procedure Estimate Original Bids. The second filters the ranks that are far from the ranks of the previous execution of automatic flight management. This second filtering step is not performed if this is the first execution of automatic flight management.

FIG. 30 is a flow diagram illustrating one embodiment of the procedure Auto Filter. The procedure begins at block 3000. At block 3002, the array ranks/bids is set equal to the result returned by the procedure Auto Filter Using Bids. One embodiment of the procedure Auto Filter Using Bids is illustrated below in conjunction with FIG. 31. At block 3004, it is determined if the variable first-execution has the logical value false. If so, at block 3006, the array ranks/bids is set equal to the value returned by the procedure Auto Filter Using Ranks. One embodiment of the procedure Auto Filter Using Ranks is illustrated below in connection with FIG. 32. At block 3008, the procedure returns the value of the array ranks/bids. The procedure ends at block 3010.

A second embodiment of the procedure Auto Filter is shown in conjunction with the pseudo code below.

Procedure Auto Filter(T_(i),ranks/bids) Assign ranks/bids = auto filter using bids(T_(i),ranks/bids); If first-execution = false   Assign ranks/bids = auto filter using ranks(T_(i),ranks/bids); End If; Return ranks/bids; End Procedure;

The procedure Auto Filter Using Bids takes as input a term T_(i) and a list of <rank, bid> tuples. It eliminates all tuples whose bid is not close to the optimal bid of T_(i) which was computed in the previous execution of automatic flight management. If this is the very first execution of automatic flight management, the system uses the estimate of the average optimal bid b_(o), which is computed by the procedure Estimate Original Bids.

The procedure loops over the list of <rank, bid> tuples and checks if the bid of the tuple is “close” to the optimal bid computed in the previous execution of automatic flight management. The current tuple is eliminated if its bid is not close. The closeness is controlled by the parameter β, which is between zero and one. If β is zero then the current tuple is eliminated if its bid is not exactly equal to the optimal bid computed for T_(i) in the previous execution of automatic flight management. If β is one, then the current tuple is eliminated if its bid more than twice the optimal bid of the previous execution.

FIG. 31 is a flow diagram illustrating one embodiment of the procedure Auto Filter Using Bids. The procedure begins at block 3100. At block 3102, it is determined if the logical variable first execution has the logical value true. If so, at block 3104, the variable last-bid is set equal to the value of the variable original-bid-estimate. Otherwise, at block 3106, the variable last-bid is set equal to the value of the array price-protected-bid for the current search term.

At block 3108, a loop operation begins, looping over the variable x from a value of one to a value equal to the length of the array ranks/bids. At block 3110, the variable bid is set equal to the bid value of the Xth entry of the array bid-of. At block 3112, it is determined if the value of bid is less than the product of one minus beta and the variable last-bid or if the value of the variable bid is greater than one plus beta multiplied by the value of the variable last-bid. If so, at block 3114, the Xth entry is deleted from the array ranks/bids. Otherwise, at block 3116, control returns to the start of the loop block 3108. After processing all values in the loop, at block 3118, the array ranks/bids is returned. The procedure ends at block 3120.

An alternative embodiment of the procedure Auto Filter Using Bids is illustrated below in conjunction with the following pseudo code.

Procedure Auto Filter Using Bids(T_(i),ranks/bids) If fist-execution = true  Assign last-bid = original-bid-estimate; Else  Assign last-bid = price-protected-bid(T_(i)); End If; Loop x from 1 to length(ranks/bids)  Assign bid = bid-of(ranks/bids,x);  If bid < (1 − β)* last-bid or bid > (1 + β)* last-bid  delete ranks/bids(x) from ranks/bids;  End If; End Loop; Return ranks/bids; End Procedure;

The procedure Auto Filter Using Ranks takes as input a term T_(i) and a list of <rank, bid> tuples. It eliminates all tuples whose rank is not close to the optimal rank of T_(i) which was computed in the previous execution of automatic flight management.

The procedure loops over the list of <rank, bid> tuples and checks if the rank of the tuple is “close” to the optimal rank computed in the previous execution of automatic flight management. The current tuple is eliminated if its rank is at a distance greater than α from the optimal rank computed for T_(i) in the previous execution of automatic flight management.

FIG. 32 is a flow diagram illustrating the procedure Auto Filter Using Ranks. The procedure begins at block 3200. At block 3202, the variable last-rank is assigned the value of the entry corresponding to the current search term in the array last/rank. At block 3204, a looping operation begins, looping over the variable x from a value one to a value equal to the length of the array ranks/bids. At block 3206, the variable rank is given the value of the rank of the Xth entry of the array ranks/bids. At block 3208, it is determined if the variable rank has a value less than the variable last-rank minus alpha or if the variable rank has a value greater than the sum of last-rank and alpha. If so, at block 3210, the Xth entry is deleted from the array ranks/bids. Otherwise, at block 3212, control returns to the start of the loop at block 3204. At block 3214, after all entries have been processed in the loop, the procedure returns the value of the array ranks/bids. The procedure ends at block 3216.

An alternative embodiment of the procedure Auto Filter Using Ranks is shown below in connection with the following pseudo code.

Procedure Auto Filter Using Ranks(T_(i),ranks/bids) Assign last-rank = last-rank(T_(i)); Loop x from 1 to length(ranks/bids)  Assign rank = rank-of(ranks/bids,x);  If rank < (last-rank − α) or rank > (last-rank + α)  delete ranks/bids(x) from ranks/bids;  End If; End Loop; Return ranks/bids; End Procedure;

From the foregoing, it can be seen that the present embodiments provide a method and apparatus providing an automated flight management system for advertisers with an on-line marketplace system. The system receives as input parameters an advertising budget, duration of the advertising flight, applicable search terms of the advertiser, a maximum average cost per action, conversion rates and the advertiser's average profit per action for each term. The system automatically sets the cost per click or other action for every listing in order to maximize the advertiser's profit and fully spend the budget over the duration of the flight. Periodically, the system automatically recomputes the cost per action of listings. This system thus enhances the convenience and efficiency experienced by the advertiser with the marketplace system.

While a particular embodiment of the present invention has been shown and described, modifications may be made. For example, the system may be applied to any on-line marketplace system using payment schemes other than cost per click, such as cost per impression, where economic value is given by an advertiser when the advertiser's search listing is displayed to a user, or pay for inclusion, where economic value is given by the advertiser just to be included in the search database. It is therefore intended in the appended claims to cover such changes and modifications, which follow in the true spirit and scope of the invention. 

1. A method for managing an advertising flight in a pay-for-placement marketplace among one or more advertisers and one or more searchers, the method comprising: maintaining a computer network-accessible database of advertiser search listings, each search listing including a bid amount, a search term and a network location identifier of an advertiser associated with the search listing, the bid amount corresponding to economic value given by the associated advertiser to an operator of the pay-for-placement marketplace when network locations of the associated advertiser are referred to a searcher in response to a database query from the searcher, search results produced from the database in response to the database query being displayable to the searcher in a rank according to the bid amounts of the search listings of the search results; receiving advertising flight parameters from an advertiser; from time to time, reviewing bid amounts for specified advertiser search listings in the pay-for-placement marketplace; and adjusting bid amounts of the specified advertiser search listings to achieve specified advertiser goals of the advertising flight parameters.
 2. The method of claim 1 wherein receiving advertising flight parameters comprises: receiving one or more advertiser search terms; receiving a flight budget; and receiving a flight interval.
 3. The method of claim 2 wherein receiving advertising flight parameters comprises: receiving one or more of a maximum average cost per click over the received one or more advertiser search terms, a conversion rate for the advertiser; and an average profit per action for the advertiser.
 4. The method of claim 1 wherein adjusting bid amounts of the specified advertiser search listings to achieve specified advertiser goals comprises: maximizing searcher contacts with a web site of the advertiser.
 5. The method of claim 1 wherein adjusting bid amounts of the specified advertiser search listings to achieve specified advertiser goals comprises: maximizing searcher clicks to web sites of the advertiser.
 6. The method of claim 5 wherein adjusting bid amounts comprises maximizing searcher clicks to web sites of the advertiser subject to a maximum cost per click.
 7. The method of claim 1 wherein adjusting bid amounts comprises: varying bid amounts to maximize advertiser profit.
 8. The method of claim 7 wherein adjusting bid amounts comprises: varying bid amounts subject to a maximum cost per click.
 9. The method of claim 7 further comprising: determining advertiser profit based on multiple searcher clicks to one or more web sites of the advertiser.
 10. The method of claim 7 further comprising: determining advertiser profit based on conversion rate.
 11. The method of claim 10 further comprising: determining search term conversion rates for a plurality of search terms having independent conversion rates.
 12. The method of claim 10 further comprising: determining search term conversion rates for a plurality of search terms at two or more ranks, search terms at each rank having independent conversion rates.
 13. The method of claim 1 wherein reviewing bid amounts comprises: reviewing all bidding combinations for one or more search terms.
 14. A article of manufacture comprising: a computer readable data storage medium; and computer program code recorded on the computer readable data storage medium for managing an advertising flight of an advertiser with a pay-for-placement marketplace, the pay-for-placement marketplace including a database of search listings associated with respective advertisers, each search listing including a search term, each search term associated with a bid amount payable by an advertiser of a plurality of advertisers when the search listing including the search term is referred to a searcher in response to a database query from the searcher, the search listing being displayable with other search listings in a rank according to the associated bid amounts of the search listing and the other search listings, the computer program code operative to access search listings of the advertiser to vary the bid amounts for specified search terms in accordance with advertiser goals for the advertising flight.
 15. The method of claim 1 wherein search listings of the pay-for-placement marketplace each have an associated rank, and wherein reviewing bid amounts comprises: determining ranks of advertiser search listings in the pay-for-placement marketplace which are not likely to achieve the specified advertiser goals; and ignoring the search listings associated with the determined ranks when adjusting the bid amounts.
 16. The method of claim 15 further comprising: ignoring ranks below a threshold rank.
 17. The method of claim 15 wherein search listings of the pay-for-placement marketplace each have an associated rank and an associated number of clicks, the method further comprising: ignoring ranks associated with search listings having fewer clicks than a click threshold.
 18. The article of claim 14 wherein the computer program code comprises code which from time to time checks conditions in the pay-for-placement marketplace and adjusts bids of advertiser search listings to ensure that advertiser-specified objectives are met.
 19. The article of claim 14 wherein the computer program code comprises code to update bid amounts of the advertiser according to advertiser-specified flight management criteria, the bid amounts being associated with advertiser search terms of a database searchable by searchers, the bid amounts representing economic value payable by the advertiser when a searcher selects a search term of the advertiser.
 20. The method of claim 1 further comprising periodically recomputing bid amounts in accordance with the flight parameters.
 21. The method of claim 1 wherein search listings of the pay-for-placement each have an associated bid amount, the method further comprising: receiving an advertiser specified maximum bid amount for one or more search listings; and adjusting the associated bid amount for the one or more search listings in accordance with the maximum bid amount.
 22. The method of claim 21 wherein adjusting the bid amount comprises: reducing an associated bid amount for a search listing to a level that just exceeds a next-lowest bid amount for the search listing.
 23. The method of claim 1 further comprising: receiving search queries from searchers; searching in the database for search terms having a match with the search queries; providing search results from the search terms having a match with the search query; when a provided search term is clicked by a searcher, transferring economic value from the advertiser associated with the provided search term.
 24. A database search system in a pay for placement marketplace, the database search system comprising: a database of search listings, each search listing including a search term, each search term associated with a bid amount payable by an advertiser of a plurality of advertisers when the search listing including the search term is referred to a searcher in response to a database query from the searcher, the search listing being displayable with other search listings in a rank according to the associated bid amounts of the search listing and the other search listings; a search engine responsive to search queries received over a computer network from searchers for searching the database; and a flight management agent responsive to advertiser-specified parameters for adjusting bid amounts of search listings to manage expenditures over a time interval.
 25. The database search system of claim 24 wherein the flight management agent is configured to receive information about one or more search terms of an advertiser, a budget, and the time interval.
 26. The database search system of claim 25 wherein the flight management agent is configured to receive information about one or more of a maximum average cost per click for the one or more search terms, a conversion rate and an average profit per action.
 27. The database search system of claim 25 wherein the flight management agent is configured to spend the budget over the time interval.
 28. The database search system of claim 25 wherein the flight management agent is configured to maximize number of clicks to a web site of an advertiser.
 29. The database search system of claim 25 wherein the flight management agent is configured to maximize advertiser profit for an advertiser over the time interval.
 30. The database search system of claim 29 wherein the flight management agent is configured to maximize the advertiser profit subject to a maximum price per click.
 31. The database search system of claim 29 wherein the flight management agent is configured to maximize the advertiser profit by determining profit based on multiple searcher actions at a web site of the advertiser.
 32. The database search system of claim 29 wherein the flight management agent is configured to maximize advertiser profit by determining profit based on a plurality of search terms having a plurality of conversion rates.
 33. The database search system of claim 29 wherein search terms in the database are each associated with a respective advertiser of the plurality of advertisers, each search term having an associated rank and wherein the flight management agent is configured to determine advertiser profit for the respective advertiser based on a plurality of search terms associated with the respective advertiser, the plurality of search terms having different conversion rates at different associated ranks.
 34. The database search system of claim 25 wherein the flight management agent is configured to analyze all possible combinations of bids for the one or more search terms and select a combination that provides maximum profit to the advertiser.
 35. The database search system of claim 25 wherein the flight management agent is configured to analyze only likely combinations of bids for the one or more search terms and select a combination from the likely combinations that provides maximum profit to the advertiser.
 36. The database search system of claim 35 wherein the flight management agent is configured to determine search term ranks not likely to provide the maximum profit to the advertiser.
 37. The database search system of claim 36 wherein search terms of the database each have an associated rank stored in the database and wherein the flight management agent is configured to exclude from further analysis search terms having an associated rank less than a rank threshold.
 38. The database search system of claim 36 wherein search listings of the database each have an associated number of clicks stored in the database, a click corresponding to a referral of the search listing to a searcher in response to a database query, and wherein the flight management agent is configured to exclude search terms having received a number of clicks fewer than a click threshold.
 39. The database search system of claim 24 wherein the flight management agent is configured to recompute the bid amounts of the search listings from time to time.
 40. The database search system of claim 24 wherein the search engine is responsive to search queries received from searchers over the Internet. 